cover letter data scientist

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Data Scientist Cover Letter Examples For 2024 (20+ Skills & Templates)

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Looking to score a job as a Data Scientist?

You're going to need an awesome cover letter. This guide is your one-stop-shop for writing a job-winning Data Scientist cover letter  using our proven strategies, skills, templates, and examples.

All of the content in this guide is based on data from coaching thousands of job seekers (just like you!) who went on to land offers at the world's best companies.

If you want to maximize your chances of landing that Data Scientist role, I recommend reading this piece from top to bottom. But if you're just looking for something specific, here's what's included in this guide:

• What To Know About Writing A Job-Winning Data Scientist Cover Letter
• The Best Skills To Include On A Data Scientist Cover Letter

How To Address A Data Scientist Cover Letter

• 3 Data Scientist Cover Letter Examples

The 8 Best Data Scientist Cover Letter Templates

3 tips for writing a job-winning data scientist cover letter.

Here's the step-by-step breakdown:

Data Scientist Cover Letter Overview: What To Know To Write A Cover Letter That Wins More Job Offers

What do companies look for when they're hiring a Data Scientist?

Companies want Data Scientists with strong technical skills in programming languages like Python or R. They also want candidates skilled in data manipulation, statistical analysis, and machine learning models.

Companies are also seeking data scientists who possess problem-solving skills and can extract actionable insights from large datasets.

Your cover letter should show the company that your personality and your experience encompass all of these things.

Additionally, there are a few best practices you want to follow to write a job-winning Data Scientist cover letter:

• Properly address your letter: Begin your letter by addressing the hiring manager or team. Don't forget to state the specific Data Scientist job you are interested in!
• Express your understanding of the company: Clearly articulate that you are well-familiarized with the company's products and the market, and tie this up with your skills, demonstrating how they align with their needs.
• Highlight your skills: Showcase your most relevant skills for the role, such as proficiency in relevant programming languages, analysis, machine learning, AI, and more.
• Tailor your cover letter for the job description: Emphasize your experience to meet the job requirements listed in the description.
• Be concise and clear: Your cover letter should be short and easy to read. Keep it to one page and focus on the good stuff!
• End with a call to action: Close with an effective call to action, expressing enthusiasm for the opportunity and inviting further discussion on how you can contribute to the company's success.
• Proofread: Make sure to thoroughly proofread your cover letter for any grammatical errors or typos. A well-written, error-free letter can make a strong first impression. I recommend using Hemingway App or Grammarly to do this.

Let's dive deeper into each of these, so you have the exact blueprint you need to see success.

The Best Data Scientist Skills To Include On Your Cover Letter

Keywords are one of the most important factors in your cover letter. They show employers that your skills align with the role, and they also help format your cover letter for Applicant Tracking Systems (ATS).

If you're not familiar with ATS systems, they are pieces of software used by employers to manage job applications. They scan cover letters for keywords and qualifications and make it easier for employers to filter and search for candidates whose qualifications match the role.

If you want to win more interviews and job offers, you need to have a keyword-optimized cover letter. There are two ways to find the right keywords:

1. Leverage The 20 Best Data Scientist Keywords

The first is to leverage our list of the best keywords and skills for a Data Scientist cover letter.

These keywords were selected from an analysis of  real Data Scientist job descriptions sourced from actual job boards. Here they are:

• Data Science
• Communication
• Machine Learning
• Engineering
• Cross-Functional
• Organization
• Collaboration
• Decision Making

2. Use ResyMatch.io To Find The Best Keywords That Are Specific To Your Cover Letter And Target Role

The second method is the one I recommend because it's personalized to your specific cover letter and target job.

This process lets you find the exact keywords that your cover letter is missing when compared to the individual role you're applying for.

Here's how it works:

• Open a copy of your updated Data Scientist cover letter
• Open a copy of your target Data Scientist job description
• In the widget below, paste the job description and hit scan!

ResyMatch is going to scan the target job description and show you the exact keywords and skills that are relevant to the role and that you should weave into your cover letter.

If you're ready to check it out, grab your target job description and run your first scan here:

To start, paste your target job description (or multiple job descriptions) into the box below:

Scan to help you identify keywords and skills within your target job description.

Scanning...

If you're more of a visual learner, here's a video walking through this whole process so you can follow along:

Personalization is what makes a cover letter stand out. That starts from the very first sentence where you greet the person reading your cover letter! There are two ways to do this well:

1. Use The Hiring Manager's Name

The first, and best, is by including the hiring manager's name. Let's say that you discovered the hiring manager's name from a post on LinkedIn or via an informational interview.

This is the jackpot! All you need to do is use their name in the introduction, like this:

2. Use This Formula: To The [Department] Team at [Company]

If you don't have the hiring manager's name, no problem! You can address your cover letter to the team that you're applying to.

For example, if you're applying for a Product Marketing Manager role at Discovery Education, you might start your cover letter like this:

This shows the reader that this letter has been written specifically for them and the content inside of it will support that.

It's much more relevant and personal than  “To Whom It May Concern!”

For more advice on writing a strong opening to your cover letter, check out this guide.

3 Data Scientist Letter Examples For 2024

Now let's take a look at all of these best practices in action. Here are three cover letter examples for different situations from people with different backgrounds who are all applying for Data Scientist roles:

Data Scientist Cover Letter Example #1: A Traditional Background

Our first example is a cover letter written by a candidate with traditional experience. Here is what an example of their cover letter might look like:

Data Scientist Cover Letter Example #2: A Non-Traditional Background

Our second cover letter example comes from a candidate looking to transition from a background in sales to a Data Scientist role. This cover letter demonstrates how the author identifies and communicates their transferable skills:

Data Scientist Cover Letter Example #3: Data Scientist New Grad

Our third example highlights a new grad who's worked on several self-initiated projects. Here is what their cover letter might look like:

At this point, you know all of the basics you'll need to write a Data Scientist cover letter that wins you more interviews and offers. The only thing left is to take all of that information and apply it to a template that's going to help you get results.

We made that easy with  our CoverBuild tool . It has 8 proven templates that were created with the help of recruiters and hiring managers at the world's best companies. These templates also bake in thousands of data points we have from the job seekers in our audience who have used them to land job offers.

Just click any of the templates below to start building your cover letter using proven, recruiter-approved templates:

Free Job-Winning Cover Letter Templates, Have AI Write Yours in Less Than 2 Minutes .

Choose a cover letter template below to get started:.

Great Choice! Now, how do you want to write your cover letter?

Let AI write your unique cover letter using our proven templates.

Or use our templates to write your own from scratch.

You're off to a strong start! But I've got a few more tips to help you take your cover letter to the next level:

1. Use CoverBuild.io To Write Your Cover Letter In <2 Minutes

All of these tips and best practices work, but you still have to implement them. Normally, that'd mean you sitting down and spending hours brainstorming ideas, typing, deleting, and typing again, and then feeling absolutely drained.

Now there's a way to work around all of that so you save your best energy for the writing and edits that matter most. Here's how it works:

• Head to CoverBuild.io
• Select the “2 Minutes” option to have AI write your cover letter
• Select a template for your cover letter, then follow the 3 step process for sharing info about your experience and the target job with CoverBuild's AI
• Click “Generate My Cover Letter” and watch the tool write a unique cover letter that's personalized to your experience and your target job!

To make it even easier, you can select a cover letter template from the widget below to get started:

If you'd like to see the process in action, here's a video walking through all of the steps above:

Note:  I do not recommend or advise that you simply copy and paste any AI-generated content and submit your application. You should always review, revise, and personalize the content yourself before applying.

2. Include Measurable Metrics And Outcomes

Too many job seekers only focus on the actions that they took and not the outcomes that resulted from those actions. As a hiring manager, it's impossible to differentiate between a dozen candidates who were all “Responsible for building applications to help achieve company goals.”

If you want to win, your cover letter should speak to the specific outcomes that you drove in previous roles. That could be:

• The accuracy of the predictive models you've developed
• The sales increase you've helped drive with data analysis
• The operational costs you helped reduce

These outcomes will show hiring teams what you're capable of and make your value crystal clear!

3. Match Your Cover Letter And Resume Design

They're the exact same car, down to the year, make, and model. The only difference is the way the product was presented. Like I said, quality impacts perceived value.

One of the best ways to boost the quality of your cover letter is to make it look clean, professional, and have it match your resume. That's why the resume templates in our resume builder tool match the cover letter templates in our cover letter builder:

If you use both tools to create your cover letter and your resume, your entire application is going to be top notch.

Key Takeaways To Wrap Up Your Job-Winning Cover Letter

You made it! We packed a lot of information into this post so I wanted to distill the key points for you and lay out next steps so you know exactly where to from here.

Here are the 5 steps for writing a job-winning Data Scientist cover letter:

• Start with a proven cover letter template from CoverBuild.io
• Use  ResyMatch.io to find the right keywords and optimize your cover letter for each role you apply to
• Start your cover letter with a personalized greeting for the hiring manager or team name
• Emphasize the measurable outcomes and value you drove in previous roles (include metrics!)
• Compare the draft of your cover letter to the examples on this page to make sure you're on the right path
• Use a tool like  Hemingway App or Grammarly to proofread your cover letter before you submit it

If you follow those steps, you're going to be well on your way to landing more Data Scientist interviews and job offers.

Now that your cover letter is taken care of, be sure to check out our guide on writing a job-winning Data Scientist resume (with examples!).

Paula Martins

Paula is Cultivated Culture's amazing Editor and Content Manager. Her background is in journalism and she's transitioned from roles in education, to tech, to finance, and more. She blends her journalism background with her job search experience to share advice aimed at helping people like you land jobs they love without applying online.

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Data Scientist Cover Letter Examples and Templates for 2024

• Cover Letter Examples

How To Write a Data Scientist Cover Letter

• Cover Letter Text Examples

When writing your data scientist cover letter , focus on how you can generate statistical insights for the organization that posted the job. Also, emphasize your related skills like algorithm testing, process streamlining, or data visualization.

This guide will help you write a cover letter that gets you interviews for your next job as a data scientist.

Data Scientist Cover Letter Templates and Examples

• Entry-Level
• Senior-Level

An effective cover letter usually has five sections, outlined below. To write a great data scientist cover letter, connect each section to the employer and its hiring needs. The following advice and examples will show you what to include in your cover letter so it’s optimized for each job application.

At the top of the page, include:

• Your resume contact header, which should give your full name, address , email, phone number, and LinkedIn if applicable
• The recipient’s name, title, organization, and contact information (when available)

Note: Feel free to omit this section if you send your letter by email. But keep your contact details below your signature. Double-check that your phone number is current and accurate and your email address matches the one you’re sending from. These simple details are easy to overlook but as important as any information on your cover letter.

Dan Doe Data Scientist | [email protected] | (123) 456-7890 | 456 Address Rd., Fort Lauderdale, FL 33319

June 2, 2024

Jenny Smith Hiring Manager Data Labs (967) 684-3230 [email protected]

2. Salutation

When possible, address your recipient by name – the quickest way to indicate you’re sending a job-specific letter and not a boilerplate. If the person’s name isn’t available, use a variation of “Dear Hiring Manager” so your greeting is still tailored somewhat to each application.

Dear Ms. Mata,

Dear Seattle Genetics Hiring Manager:

3. “Hook” or introduction

Catch the reader’s attention by citing an achievement from your resume or a key credential the company seeks in applicants. Then, connect that highlight to your main strengths and overall candidacy for the advertised data scientist role. Refer to the hiring organization by name here to further convince the recipient you’re applying to them specifically.

You can also make your introduction stand out by networking. Cite any personal or professional connections you have with the hiring manager. If someone at the organization alerted you to the job, use a line like “I was excited to hear from your colleague [Name] about [Company]’s new data scientist position.” Or, if you’ve already met your recipient, mention that with a line like “It was great speaking with you at the [event] last month.”

My shortlisting for a Beckenbach Book Prize in 2023 for my publication on applied mathematics was my proudest achievement to date as a mathematician. I hope to demonstrate my extensive knowledge of mathematical modeling and strong leadership style in the principal mathematician role at Edivna.

4. Body paragraph(s)

On a separate document or sheet of paper, take 10 minutes to brainstorm why this data scientist position or employer interests you. What details on the job posting caught your attention, and how do they compare to your experience?

When you’re done brainstorming, review your notes: Do any stand out as important or persuasive? Take another 10 minutes to brainstorm and elaborate on them. Repeat this process until you have at least two or three concise sentences that speak to the job opening at hand. By making these sentences a central part of your letter, you’ll have the best chance of persuading the manager to give you a call.

Following this explanation, cite a few more of your data scientist skills or achievements , possibly as bullet points.

With three years of data science experience and two promotions under my belt, I was attracted to the position at Data Labs due to the company’s reputation for high performance. I also appreciate Data Lab’s approach to employee well-being and feel I would thrive within the company.

If I were to secure this role, I plan to bring even more success to Data Labs. Some of my past responsibilities and achievements include:

• Improving the efficiency of data collection techniques, resulting in 15% faster collation
• Undertaking a new approach to data regression analysis, which helped the company predict future sales with a 95% accuracy rate

Finally, request an interview and thank the hiring manager for their time and attention. Consider briefly restating why this job opportunity interests you. You can also use this section to refer to your resume or other application materials. To end your cover letter, use a simple closing like “Sincerely” or “Best regards” and then your name.

I have the qualities necessary to become an inspiring and effective leader at Seattle Genetics and would appreciate discussing the position with you further. Please don’t hesitate to contact me to schedule a meeting.

Diana Kaiser

Data Scientist Cover Letter Tips

1. show you can derive data-backed insights.

Start your letter strong with a clear example or measure of your success as a data scientist. Consider how you’ve developed or bettered companies’ use of data and how those improvements helped clarify business issues or opportunities.

Since undertaking my current data scientist role at Science Labs Corporation, I have successfully expanded the company’s data by 30%. I achieved this by incorporating third-party sources and using my initiative, for which I receive regular compliments. I hope to bring this same level of success to Data Labs as a data scientist.

2. Tell why you’re drawn to this data scientist job

For example, maybe the role requires executive consulting and creative data visualization, two focus areas of your most recent job. Or maybe you identify with the firm’s stated values around data accuracy and confidentiality.

With eight years of specialist training in the field and over two years in a leadership role, my skills and experience make me the perfect fit for Seattle Genetics’ prominent position in the genetic research sphere.

3. Highlight your technical and soft skills

Add a short list of bullet points to show your success in related work areas for your target job, such as data gathering, client relations, or process streamlining. Also, consider using this section to present your expertise in web-scraping, data visualization, or other relevant software programs.

My recent accomplishments include:

• Leading a team to develop an algorithm that directly contributed to a revenue increase of 43%
• Streamlining and improving analytical processes to reduce cases of fraud and abuse by 67%
• Creating an innovative system to solve client problems that led to the development of the company’s most successful product to date

Data Scientist Text-Only Cover Letter Templates and Examples

Dear Jenny Smith,

I would like to arrange an interview to discuss how my skills could help further Data Lab’s already impressive reputation for success. I look forward to hearing from you.

Best regards,

Diana Kaiser Lead Statistician | [email protected] | (123) 456-7890 | 321 My Road, San Francisco, CA 12345

June 1, 2024

Habibah Mata Senior Hiring Manager Seattle Genetics (111) 222-3333 [email protected]

Being shortlisted for the International Prize in Statistics for innovations in the field of clinical trial statistical methods was the pinnacle of my career to date. I hope to bring the same revolutionary thinking and attention to detail to the Principal Statistician role at Seattle Genetics.

With eight years of specialist training in the field and over two years in a leadership role, my skills and experience make me the perfect fit for Seattle Genetics’ prominent position in the genetic research sphere. My key achievements include:

• Designing a software program to improve bioinformatics for lung cancer detection, resulting in a multimillion-dollar research grant
• Achieving 25+ publications in genetic statistics and computation
• Spearheading a team to develop methodologies that improved data analysis accuracy by 45% in 2020

Bethanie Stafford Principal Mathematician | [email protected] | (567) 890-1234 | 456 Address Road, San Francisco, CA 54321

June 4, 2024

Lilian Irwin Senior Hiring Manager Edivna (789) 012-3456 [email protected]

Dear Ms. Irwin,

With 10+ years’ experience managing multiple projects and large teams, I’m intrigued by Evidna’s track record for industry excellence and results-focused philosophy. I believe that my proven ability for accuracy and efficiency and extensive experience make me the ideal candidate. My recent accomplishments include:

I would like to arrange a meeting to explore how my broad range of analytical skills and inspiring leadership can drive the mathematical team at Edivna to even greater triumphs in 2021. Please get in touch to arrange a suitable interview time.

Bethanie Stafford

Data Scientist Cover Letter Frequently Asked Questions

Do hiring managers still read cover letters -.

Some don’t, but many do. And all hiring managers seek fast ways to cut a large pile of applications down to just a few promising candidates. One common way they do this is by focusing on applications that include cover letters, then on cover letters that speak to their hiring needs. Send a custom cover letter with your resume, and you can be confident you’d end up in that select group.

How should I format my cover letter? -

Like your resume. Carry over that document’s basic format settings , such as font style, line spacing, and page margins.

How long should a data science cover letter be? -

No more than one page, or around 250 words. Resist the urge to tell your whole career story, even if you have extensive background in your target role. Give just enough detail to pique hiring managers’ interest so they look closely at your resume.

Craft a new cover letter in minutes

Get the attention of hiring managers with a cover letter tailored to every job application.

Jacob Meade

Certified Professional Resume Writer (CPRW, ACRW)

Jacob Meade is a resume writer and editor with nearly a decade of experience. His writing method centers on understanding and then expressing each person’s unique work history and strengths toward their career goal. Jacob has enjoyed working with jobseekers of all ages and career levels, finding that a clear and focused resume can help people from any walk of life. He is an Academy Certified Resume Writer (ACRW) with the Resume Writing Academy, and a Certified Professional Resume Writer (CPRW) with the Professional Association of Resume Writers & Career Coaches.

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How to Write a Data Scientist Cover Letter (With Template)

Key takeaways

• A cover letter can be the most important element in a job application. Ensuring your profile stands out to recruiters is crucial to your professional success.
• A well-tailored cover letter should provide relevant information clearly and concisely. Focus on detailing your skills and why you are the right person for that specific role.
• The included Data Scientist cover letter template provides an easy starting point to craft your own cover letters. Adapt and personalize it to fit your profile.

A well-written cover letter is key to quickly getting the attention of prospective employers. Among countless job seekers, resumes, and application letters, yours need to stand out on first impression if you want to ensure your job search   translates to a new role .

In this post, you will discover:

• Reasons why a well-crafted cover letter is key to professional success, from entry-level roles to senior positions
• Cover letter do’s and dont’s
• A Data Scientist sample cover letter you can easily adapt and personalize

A well-tailored   cover letter : The key to   job application   success

Ensuring you know how to write a cover letter that is clear, informative, and tailored to the role you are applying to will benefit you in many ways. Well-crafted cover letters have many benefits, which include:

• Showcasing relevance:   Tailoring your cover letter allows you to emphasize the most relevant skills, experiences, and achievements that align with the specific job requirements. This immediately captures the attention of the   talent acquisition   team, recruiters, or human resources reps.
• Demonstrating research:   A good cover letter conveys your understanding of the organization's needs and illustrates how you can contribute to its success, signaling to potential employers that you've done your homework.
• Telling your story:   Each job application is unique, and a tailored cover letter enables you to craft a personalized narrative. It lets you connect your professional journey with the role's specific challenges and opportunities, making your application more compelling.
• Highlighting cultural fit:   Your cover letter allows you to address the company's values, mission, and culture. By aligning your experiences and values with those of the organization, you demonstrate a cultural fit and convey your enthusiasm for being part of the team.
• Addressing specific requirements:   Job postings often include   specific skills or qualifications   the employer is seeking. Tailoring your cover letter enables you to address these requirements directly, showcasing how you possess the desired attributes and can meet the company's expectations.

Cover letter tips

A great cover letter should reflect your professional profile and personality. However, no matter what your cover letter's content is, the tips below will help ensure the message you want to convey is clear and easily accessible to hiring managers.

• Keep it concise:   Aim for a cover letter length of 250-400 words. Be succinct in presenting your qualifications and experiences.
• Use a clean layout:   Opt for a professional and clean cover letter format with a standard font (e.g., Arial, Calibri, or Times New Roman) and a font size of 10-12 points.
• Include   contact information :   Provide your contact information at the top of the cover letter, including your name, phone number, and professional email address.
• Use   headers   and sections:   Organize your cover letter into clear sections with headers such as Introduction, Work Experience, and Achievements for easy readability.
• Maintain a professional tone:   Keep the tone of your cover letter professional and upbeat. Avoid overly casual language, and focus on showcasing your skills and experiences.
• Use keywords:   Incorporate relevant keywords from the Agile Project Manager   job description   and company website into your cover letter. This can help your application pass through   applicant tracking systems (ATS)   used by many employers.
• Highlight achievements with bullet points:   Use bullet points to list specific accomplishments or notable projects. This makes it easier for the reader to grasp your accomplishments quickly.
• Use quantifiable data:   Whenever possible, include quantifiable data to demonstrate the impact of your achievements. Numbers provide concrete evidence of your contributions.
• Match company tone:   Adapt your writing style to match the tone of the company and industry. Research the company's culture to strike the right balance between professionalism and personality.
• Showcase company knowledge:   Demonstrate your understanding of the company by referencing its values, mission, or recent achievements. Explain why you're excited about the opportunity to work for this specific organization.
• Address employment gaps (if applicable):   If you have employment gaps, briefly address them in a positive light, focusing on any skills or experiences gained during those periods.
• Proofread   thoroughly:   Eliminate typos and grammatical errors by proofreading your cover letter multiple times. Consider using tools like Grammarly to catch any overlooked mistakes and ensure your English (or any language you use) is correct.
• Include a   call to action :   Conclude your cover letter with a call to action, expressing your enthusiasm for the opportunity and indicating your readiness for an interview.
• Follow submission instructions:   If there are specific instructions for submitting the cover letter, such as naming conventions or document formats, ensure that you adhere to them.
• Save as a PDF:   Save your cover letter as a PDF before submitting it. This ensures that the formatting remains consistent across different devices and software.

While understanding the correct steps to write a cover letter is crucial to your professional success, knowing what mistakes to avoid is equally important. The best cover letter can easily be made useless by a tiny blunder. Avoid making the mistakes listed below; you will be halfway to your new job.

• Don't use a generic greeting:   Avoid generic salutations like "To whom it may concern," “Dear sir or madam, “ or “Dear hiring manager.“ Whenever possible, address the cover letter to a specific person.
• Don't repeat your resume:   An effective cover letter should complement your resume, not duplicate it. Focus on specific experiences and achievements that showcase your qualifications for the role.
• Don't exaggerate or lie:   Be truthful in your cover letter. Exaggerating your qualifications or providing false information can harm your chances and damage your professional reputation.
• Don't use unprofessional email addresses:   Ensure that the email address you use in your contact information is professional. Avoid using nicknames or unprofessional terms.
• Don't include irrelevant information:   Keep your cover letter focused on the job. Avoid including unrelated personal details or experiences that do not contribute to your suitability for the role.
• Don't use jargon unnecessarily:   While demonstrating your knowledge is essential, avoid unnecessary jargon that may confuse the reader. Use clear and straightforward language.
• Don't sound overly eager:   Expressing enthusiasm is positive but can easily feel unauthentic if overdone.

Remember, the goal of a practical cover letter is to present your qualifications in a clear, organized, and compelling manner while adhering to professional standards.

How to structure your Data Scientist cover letter

Express your genuine interest in the   Data Scientist   position in the opening paragraph. Mention your enthusiasm for leveraging advanced analytical techniques and your eagerness to contribute to a team focused on extracting valuable insights from complex datasets. Take a moment to acknowledge any referrals that may have influenced your decision to apply.

About your current role

Illustrate your impact by highlighting concrete achievements and innovative strategies you've implemented to enhance data analysis and contribute to the success of your team. Emphasize your role in deploying specific data science methodologies or tools that led to measurable outcomes, showcasing your proficiency in driving positive analytical change.

Use this section to shed light on your current responsibilities and ongoing projects, drawing connections to how they align with the requirements and objectives of the Data Scientist role.

About your experience

Detail your hands-on experience in leading data-driven initiatives, ensuring adherence to statistical methodologies, and fostering a culture of continuous improvement. Clearly communicate that your analytical skills and readiness for the role are well-established. 

This section is also the place to highlight your years of experience in the data science field, along with any additional technical or soft skills acquired throughout your   career path .

Notable achievements

Showcase standout accomplishments that exemplify your skills as a Data Scientist. Whether you played a crucial role in solving a complex data challenge, contributed to the delivery of impactful insights, or implemented an innovative solution, use this section to concisely mention your achievements, how they were measured, and their impact on the team or organization.

Why you want to work there

Highlight specific aspects of the company's reputation, mission, and values related to data science that resonate with you. Explain how these align seamlessly with your professional goals and how you envision contributing to the company's success through your expertise in data science. Be concise but expressive about your motivations.

Specific projects or initiatives that motivated you to apply

Demonstrate your understanding of the organization by referencing specific data science projects or initiatives that have captured your interest. Link these initiatives to your skills and experiences, emphasizing how your contributions align with the company's goals. This shows your genuine interest and proactive approach to aligning with the company's mission.

Use the closing paragraph to reiterate your eagerness to contribute to the company's success as a Data Scientist, express your excitement about the possibility of further discussing how your skills align with the company's objectives, and encourage the reader to   reach out with any questions   they may have. Sign off with a professional salutation.

Data Scientist   cover letter template

Dear [Hiring Manager’s name],

I am writing to express my enthusiastic interest in the Data Scientist position at [Company Name], as advertised. With a strong background in data science and a proven track record of leveraging analytics to derive meaningful insights, I am confident in my ability to make significant contributions to your organization.

About my current role

In my current position as a Data Scientist at [Current Company], I have:

• Applied advanced statistical and machine learning techniques to analyze large datasets, resulting in [specific outcome, e.g., improved predictive accuracy, cost savings, etc.].
• Developed and implemented predictive models that led to a [percentage] improvement in [relevant metric], showcasing my commitment to delivering actionable insights.
• Collaborated with cross-functional teams to interpret data and provide strategic recommendations, contributing to [specific project or business goal].

About my Data Scientist experience

My experience extends to:

• Conducting exploratory data analysis to identify trends and patterns, leading to [measurable outcomes, e.g., improved decision-making].
• Utilizing programming languages such as Python and R to clean, manipulate, and analyze data, ensuring accuracy and reliability in findings.
• Communicating complex technical findings to non-technical stakeholders, fostering a data-driven decision-making culture within the organization.

Some of my notable achievements include:

• Implementing a data-driven strategy that resulted in a [percentage] improvement in [specific business metric], positively impacting overall business performance.
• Developing and deploying a machine learning model that optimized [specific process], leading to significant time and resource savings.
• Collaborating on [specific project] that involved [unique or challenging aspect], demonstrating my adaptability and problem-solving skills in a dynamic environment.

Why I want to work for [Company]

I am particularly drawn to [Company Name] due to its [mention aspects unique to the company and are a core part of its mission and values such as: commitment to innovation, data-driven decision-making, diversity,...]. I am excited about the opportunity to apply my data science skills to contribute to [Company Name]'s continued success and growth.

Specific projects or initiatives of [Company] that motivated me to apply

In researching [Company Name], I was impressed by your recent initiatives in [specific data-related project or achievement]. I believe my expertise in data science aligns with your organizational objectives. My passion for extracting meaningful insights from data and my commitment to delivering high-quality results would make me a valuable addition to your team.

Thank you for considering my application. I am eager to further discuss how my skills and experiences align with the Data Scientist role at [Company Name]. I look forward to contributing to your team's success.

[Your Full Name]

Get your career rolling with Deel

Your job application is your chance to tell your professional story, and a well-tailored cover letter is your narrative's opening chapter. Remember that personalization is key. Make each word count, emphasizing how your background uniquely positions you as the ideal candidate, and get your dream job. 

Looking for even more inspiration?   Discover how to write a stellar cover letter in 5 steps .

Discover more tips and tools to help boost your career further and climb the steps to your dream job on   the get-hired content hub .

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14 Professional Data Scientist Cover Letter Examples for 2024

Your data scientist cover letter must quickly capture the employer's attention. Highlight your expertise in managing and analyzing large sets of data. Demonstrate your proficiency with statistical analysis and machine learning in real-world applications. Show your ability to communicate complex findings effectively to non-technical stakeholders.

All cover letter examples in this guide

Data Science Intern

Entry-Level Data Scientist

Senior Data Scientist

Machine Learning

Python Data Scientist

Data Science Manager

Associate Data Scientist

Data Science Director

Educational Data Scientist

Data Analytics Scientist

Data Science Consultant

Metadata Scientist

NLP Data Scientist

Cover letter guide.

Data Scientist Cover Letter Sample

Cover Letter Format

Cover Letter Salutation

Cover Letter Introduction

Cover Letter Body

Cover Letter Closing

No Experience Data Scientist Cover Letter

Key Takeaways

By Experience

Crafting the perfect data scientist cover letter can be a puzzling task when you're deep in the job hunt. You've been submitting applications and suddenly hit the cover letter hurdle – it's not just a repeat of your resume but a snapshot of your crowning professional achievement. In this guide, we'll show you how to weave in your triumph without drowning in clichés or penning a novel. Keep it concise, captivate with clarity, and let your story shine on just one page.

• Introduce your profile to catch recruiters' attention;
• Use professional templates and examples to make sure your data scientist cover letter follows the best industry standards;
• Settle on your most story-worthy achievement to shine a light on what makes your application unique;
• Write a data scientist cover letter, even when you lack professional experience.

Ready to start with the basics: upload your resume to Enhancv's AI, below, to see the data scientist cover letter it would write for you.

If the data scientist isn't exactly the one you're looking for we have a plethora of cover letter examples for jobs like this one:

• Data Scientist resume guide and example
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• Junior Data Analyst cover letter example
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• Data Science Manager cover letter example
• Data Engineer cover letter example
• Statistical Data Analyst cover letter example
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• Data Analytics Manager cover letter example
• Data Specialist cover letter example
• Data Entry Clerk cover letter example

Data Scientist cover letter example

Charlotte May

Phoenix, AZ

+1-(234)-555-1234

[email protected]

• Illustrate previous success with concrete results: The cover letter highlights a key achievement at Dell, where a revamp of the digital marketing strategy led to a 50% improvement in campaign performance and a 250% spike in revenue from search marketing campaigns, which shows the candidate's ability to drive substantial business outcomes.
• Match skills to job requirements: The cover letter mentions specific expertise in machine learning and data analytics, aligning directly with the skills sought in a data scientist role, signaling the candidate's suitability for the technical aspects of the job.
• Show strategic impact: The candidate emphasizes their ability to not just analyze data but also make strategic recommendations, showing that they have a broader vision for how data science can influence and grow the business.
• Express eagerness to contribute to the company's success: The cover letter stresses the candidate's enthusiasm to bring their track record to the new company, indicating a future-focused, proactive mindset that is likely to resonate with the hiring manager.

The format of your data scientist cover letter: structure, fonts, margins, and more

Your data scientist cover letter should include a header (with your name, position, and date); a greeting and introductory paragraph; a body and closing paragraphs; and an optional signature.

Remember that you're writing your data scientist cover letter for recruiters - as the Applicant Tracker System won't scan this content.

Here are a few more tips and tricks to keep in mind when formatting your data scientist cover letter:

• Use the same font in your data scientist cover letter and resume . We recommend modern fonts, e.g. Lato and Rubik, to help you stand out, instead of the stereotypical Arial and Times New Roman.
• Each paragraph should have single spacing, which is already set up for you in our cover letter templates .
• Our cover letter builder follows industry standards for your data scientist cover letter formatting - with a one-inch margin, surrounding your content.
• Always export your data scientist cover letter in PDF to ensure the image or text quality stays the same and your writing isn't moved about.

The top sections on a data scientist cover letter

• Header: Includes full name, address, phone number, email, and date, setting a professional tone and providing the recruiter with essential contact information.
• Greeting: Addresses the hiring manager or recruitment team by name if possible, demonstrating attention to detail and personalized communication, which reflects a data scientist's precision.
• Introduction: Briefly summarizes the candidate's background in data science, stating the intention to apply and a hook on why they're a good fit, piquing the recruiter's interest.
• Experience and Skills: Highlights relevant data science projects, technical skills like programming languages and statistical analysis, and problem-solving experiences that are critical for the role.
• Closing and Call to Action: Reiterates interest in the position, invites the recruiter to review the attached resume, and suggests a meeting or interview, showing proactiveness and enthusiasm for the role.

Key qualities recruiters search for in a candidate’s cover letter

• Proficiency in programming languages such as Python or R: Necessary for writing scripts to manipulate data and build statistical models.
• Mastery of data wrangling and preprocessing: Ensures the candidate can efficiently clean and organize raw data for analysis.
• Experience with machine learning algorithms and statistical analysis: Demonstrates the ability to apply advanced techniques to extract insights from data.
• Familiarity with data visualization tools (e.g., Tableau, PowerBI) and libraries (e.g., Matplotlib, Seaborn): Important for communicating findings effectively to stakeholders.
• Knowledge of big data platforms (e.g., Hadoop, Spark): Shows that the candidate can work with large datasets and understands the technical challenges involved.
• Strong problem-solving skills and business acumen: Indicates the ability to understand business problems and provide actionable data-driven insights.

How to start your data scientist cover letter: with a greeting, of course

Have you ever considered just how powerful a personalized salutation can be?

We sure have news for you! Your data scientist cover letter should start with the right salutation to recruiters, nurturing a sense of respect and individuality.

Greet recruiters by using their first name (e.g. "Dear Tom" or "Dear Patricia") if you've previously established contact with them.

Otherwise, opt out for the less familiar, "Dear Ms. Peaches" or "Dear Ms Kelsey", if you've found the recruiter's name on LinkedIn or a corporate website.

"To whom it may concern" is never a good option, as it creates a sense that you've been sending out your data scientist cover letter to anyone. Instead, use "Dear HR team" or "Dear (company name) recruiter" for a feeling of exclusivity.

List of salutations you can use

• Dear Hiring Manager,
• Dear [Company Name] Recruiter,
• Dear [Department] Team,
• Dear Mr./Ms. [Last Name],
• Dear Dr. [Last Name],
• Dear Search Committee,

How to start your data scientist cover letter introduction

The opening paragraph of your data scientist cover letter can seem like a real enigma.

Where do you start writing ?

In your data scientist cover letter introduction, focus on yourself by stating what:

• gets you motivated and excited about the role;
• you like best about the company, from culture to awards.

Write no more than two sentences, which are both authentic and show your enthusiasm for the opportunity.

Structuring your data scientist cover letter body to add more value

You've hinted at your value as a professional (this may be your passion for the job or interest in the company) in your introduction.

Next, it's time to pan out the body or middle of your data scientist cover letter .

When creating your resume, you've probably gone over the advert a million times to select the most relevant skills.

Well, it's time to repeat this activity. Or just copy and paste your previous list of job-crucial requirements.

Then, select one of your past accomplishments, which is relevant and would impress hiring managers.

Write between three and six paragraphs to focus on the value your professional achievement would bring to your potential, new organization.

Tell a story around your success that ultimately shows off your real value as a professional.

Finishing off your data scientist cover letter with what matters most

So far, you've done a fantastic job in tailoring your data scientist cover letter for the role and recruiter.

Your final opportunity to make a good impression is your closing paragraph.

And, no, a "Sincerely yours" just won't do, as it sounds too vague and impersonal.

End your data scientist cover letter with the future in mind.

So, if you get this opportunity, what do you plan to achieve? Be as specific, as possible, of what value you'd bring to the organization.

You could also thank recruiters for their interest in your profile and prompt for follow-up actions (and organizing your first interview).

Is it beneficial to mention that you have no experience in your data scientist cover letter?

Lacking professional experience isn't the end of the world for your data scientist cover letter .

Just be honest that you may not have had roles in the industry, but bring about so much more.

Like, your transferable skills, attained thanks to your whole work and life experience (e.g. the skills your summer spent working abroad taught you).

Or, focus on what makes you, you, and that one past success that can help you stand out and impress recruiters (think of awards you've attained and how they've helped you become a better professional).

Alternatively, write about your passion and drive to land the job and the unique skill set you would bring to enhance the workplace culture.

Key takeaways

Within this Enhancv guide, we've provided you with plenty of advice and inspiration on writing your data scientist cover letter:

• Always make sure your data scientist cover letter is tailored to the role you're applying for to make a good impression on recruiters;
• In your data scientist cover letter include a header (with your name, the role you're applying for, date, and contact details) and an introduction of up to two sentences that highlight your key accomplishment or why you'd fit the role;
• Focus your data scientist cover letter body on one sole achievement through your career and all the valuable lessons, skills, and know-how you've learned (that are relevant to the role);
• Ensure your data scientist cover letter closing statement isn't generic and includes either a call to action or a promise;
• If you lack professional experience, shift recruiters' focus to a relevant achievement (thanks to your academic or versatile experience) or toward your dreams and goals for professional growth.

Data Scientist cover letter examples

Explore additional data scientist cover letter samples and guides and see what works for your level of experience or role.

Cover letter examples by industry

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Enhancv harnesses the capabilities of ChatGPT to provide a streamlined interface designed specifically focused on composing a compelling cover letter without the hassle of thinking about formatting and wording.

• Content tailored to the job posting you're applying for
• ChatGPT model specifically trained by Enhancv
• Lightning-fast responses

Getting Hired in the Blockchain and Cryptocurrency World

Ideal cover letter length: how long should a cover letter be, can i handwrite my resume, resume for a 16-year-old, do you indent paragraphs in a cover letter, should you use first person in a resume – a quick guide.

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Data Scientist Cover Letter Sample and Template (2024)

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Data Scientist Cover Letter Template

Writing an outstanding data scientist cover letter is a vital step in your job search journey.

When writing a cover letter, make sure to tailor it to the specific job posting:

• research your target compan y and use your cover letter to highlight how they’ll benefit from hiring you;
• explain how your skills and qualifications will help the employer reach their business goals.

The following data scientist cover letter example will help you write a cover letter that best underscores your qualifications and experience .

You can download this template easily and personalize your letter in minutes!

Once you’re ready, all you have to do is pair it with your resume and submit your job application with confidence.

Data Scientist Cover Letter Template (Text Format)

HIRING MANAGER’S NAME

HIRING MANAGER’S JOB POSITION

COMPANY’S NAME

ADDRESS OF HIRING COMPANY

Dear [Mr./Mrs./Ms.] [Hiring Manager’s Name],

As a Master in Computer Science and a data scientist with 3 years of experience, I have become quite skilled at predictive modeling, machine learning, and advanced analytics. I am glad to count employing advanced machine learning techniques to predict the sales of new services with a 97% accuracy rate among my numerous professional accomplishments.

I would be happy to bring my robust skillset and the highest quality of service to [Company Name] as the next Data Scientist. And I am confident that my expertise will support your need for customized machine learning solutions including data querying and knowledge extraction.

At present, I am a Data Scientist at [Current Employer], a company with an established reputation in [industry/area of service].

My wins at [Current Employer] include:

• Creating and implementing data models that contributed to achieving 25% higher returns compared to previous years
• Developing workflows for conducting comparative analysis among diverse data sources and generalized approaches developed both in-house and externally

I look forward to discussing with you how my skills and experience can successfully translate to higher returns at [Company Name] and achieving the company’s most ambitious data science goals.

[Your Name]

Related Resume and Cover Letter Resources

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Home » Job Tips » Resume Writing Tips » Data Scientist Cover Letter

Data Scientist Cover Letter: Writing Tips, Template & Samples to Get Hired

An effective cover letter can help you stand out from other applicants and get the hiring manager’s attention. For data scientists, the cover letter is an opportunity to showcase their skills, experience, and passion for working with data. An excellent cover letter can help set you apart from other applicants and land an interview. In this blog, we will explore the essential components you need to craft a perfect data scientist cover letter that showcases your value and helps you land your next data scientist role.

Table of Contents

Essential Elements Of A Professional Data Scientist Cover Letter

An effective cover letter for a data scientist is very important when applying for a data scientist job role. It should include the following key sections:

The first part of the cover letter should be the header, which introduces you professionally. It is important to make a good first impression with a professional, organized cover letter by:

• Adding your full name, job title (Data Scientist), email, phone number, and date. 
• Make your name stand out in a larger font. If emailing, the subject line could say “Data Scientist Job Application – [Your Name]” so it’s easy to identify.  
• Starting with an opening paragraph expressing your excitement for the position. 
• Highlighting your relevant background – education, skills, experience – to show you are qualified.

2. Greetings/Salutation

The greeting at the top of your cover letter is important when applying for a data science job. It’s the first thing the hiring manager sees, so it needs to make a good impression by maintaining a format. Here are some guidelines to draft the perfect greetings: 

• Don’t use a generic greeting like “To whom it may concern.” 
• Do some research to find the hiring manager’s name. The best option is to address them directly with “Dear Mr./Ms. [last name].” 
• If you don’t know their name, “Dear Hiring Manager” works too. This personalized greeting shows you care about details and sets the right tone.

Make sure the greeting is respectful and professional. That positive first impression can make your cover letter stand out from the start.

3. Introduction

The introduction plays a crucial role in your cover letter as it establishes the overall tone. Your introduction should immediately capture the hiring manager’s attention and interest them enough to continue reading. Here are some tips to draft a good introduction:

• It is important to express enthusiasm for the job and convey a genuine passion for data science right from the beginning. 
• State which position you are applying for, and mention relevant educational background, degrees, or certifications in data science or related fields to demonstrate suitability. 
• Restrict your introduction to a few sentences that clearly state who you are, specify the desired position, and highlight your relevant skills. 
• To catch the attention of hiring managers and keep their interest, it is advisable to be concise yet compelling when introducing yourself.

This section is central to your application and provides an opportunity to demonstrate your skills, experiences, and passion for the role. To draft the perfect body content for your cover letter:

• Emphasize technical proficiency and problem-solving abilities. 
• Support these claims with specific examples from previous roles. 
• Talk about your professional experience and education through points that focus on relevant skills without duplicating information found in your resume or portfolio. 
• Rather than repeating facts about yourself, use this space wisely to offer new perspectives on various accomplishments. 
• Use the STAR technique to illustrate how you positively made an impact on companies you have worked at previously.

The closing paragraph of your cover letter is very important it is the last thing the hiring manager reads, so you want to leave a great final impression. You can craft your closing statement by referring to the following guidelines:

• Recap your qualifications, skills, and experience that make you a strong candidate for the job. 
• Re-emphasize that you’re truly excited for the opportunity and eager for the next steps in the process. 
• As a method to be remembered, so end with confidence that your abilities make you a great fit. 
• Maintain a professional but enthusiastic tone, and add a call to action – for example, your availability for an interview or willingness to provide more info.

6. Signature

Ensure to end your cover letter with a signature. The signature is a critical final section of your cover letter that formally closes your communication and identifies it was written directly by you. Signing the letter adds an element of personalization and professionalism. Here’s what you need to keep in mind before adding your signature:

• Your signature should match the name you used in the greeting and be written in pen for an authentic look and feel. 
• Use your full legal name for formality.

Data Scientist Cover Letter Template

A template can assist you in crafting a cover letter that presents your professional experience, achievements, and potential to recruiters with clarity and professionalism. Here is a template for your best data scientist cover letter:

Sample Cover Letter for Data Scientist

Here is a data scientist cover letter example that can serve as a helpful guide if you’re looking to write a perfect data science cover letter.

Tips to Draft the Best Data Scientist Cover Letter

To craft an engaging data scientist cover letter that gets results, keep these tips in mind:

1. Mention Relevant Skills And Experience

In your cover letter, emphasize relevant skills and experiences. For data scientists, focus on technical abilities such as programming languages, data analysis, machine learning, and statistical modeling. Don’t forget to mention soft skills like problem-solving skills and effective communication. Provide specific examples from previous roles to demonstrate skill usage and results achieved.

For instance, highlight any online data science placement guarantee course that you may have taken to upskill. Mention the technical skills like Python programming, and machine learning frameworks you used in past data analyst roles.

2. Talk About Your Current Role

To write a compelling cover letter for a data science job, it’s important to effectively explain your current role. Start by giving a summary of what you do and focus on the key tasks and accomplishments that demonstrate your expertise in handling and analyzing data. Highlight specific projects where you applied your skills successfully and made an impact. 

For example, you can state that you’re, “Currently serving as lead data scientist driving analytics strategy that increased click-through rate by 10% and revenue by 8%”.

3. Demonstrate Your Suitability for the Role

Understanding the position you’re applying for is crucial in crafting a successful cover letter. Thoroughly read and comprehend the job description to tailor your letter accordingly, emphasizing how your skills and experiences align with what the employer seeks. 

For example, focus on showcasing your experience in developing applications using the most preferred programming language that aligns with the role you’re applying for. By doing so, you convey that you have done thorough research and are committed to pursuing this opportunity.

4. Highlight Your Passion For Data Science

When writing a cover letter as a data scientist, it’s important to highlight your passion for the field. Start by expressing genuine enthusiasm for working with data and its power in solving complex problems.

Further, mentioning any relevant personal projects or research conducted outside of professional responsibilities will demonstrate to the employer that you are not simply seeking any job but specifically aspiring towards a career in data science .

5. Mention Specific Projects That Motivated You To Apply

When highlighting your relevant experience, be sure to call out specific data science projects that sparked your interest or passion for the field. For instance, you could describe a past project that motivated you to continually enhance your skills in the field. Sharing real-world examples of data projects that inspired your interest and path in data science helps convey your passion for the work.

6. Link Your Soft And Hard Skills To Your Achievements

It’s important to connect your soft and hard skills with tangible achievements. By illustrating specific accomplishments that demonstrate your abilities, you can showcase both your expertise and the real-world results of your skills.

For instance, “I used my communication and Excel skills to create sales dashboards that identified trends and opportunities, increasing team productivity by 15%.” This approach validates your abilities and shows employers how you can translate them into real-world results.

7. Follow A Clear, Concise, And Professional Format

Your cover letter is a formal document, not an essay. Keep it brief and straightforward by using plain language that everyone can understand. Avoid jargon or overly technical terms. Additionally, remember to review your letter for any spelling or grammar errors as a well-written and mistake-free cover letter creates a positive initial impression.

8. Finish Strong With A Memorable Conclusion

Your cover letter’s conclusion is your last opportunity to leave an impression. Use it to emphasize your interest in the position and why you are a good fit for it. You can also mention when you are available for an interview and express enthusiasm about discussing the role further. This will demonstrate that you take initiative and are genuinely interested in this job opportunity.

For example, “I am excited about the opportunity to contribute my skills to [Company Name]. With a passion for data science, I am eager to bring my [specific skills] to drive success and thrive in a dynamic team environment. Thank you for considering my application.”

An engaging, well-crafted data scientist cover letter is vital when applying for data scientist jobs. It provides an opportunity to make a great first impression and highlight both your technical expertise and passion for the field. Use the tips and examples in this post to craft a cover letter that gets your data science career off to a strong start. 

How do you prepare a cover letter? Share in the comments. Also, check out our blog on the best cover letter formats .

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Shailja Kaushik has been an Editor with Internshala since March 2023. She loves creative writing and experimenting with different forms of writing. She has explored different genres by working with journals and radio stations. She has also published her poems and nano tales in various anthologies. She graduated at the top of her class with Bachelor's in English and recently completed her Master's in English from the University of Delhi. Her experiments with writing continue on her literary blog.

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Data Scientist Cover Letter

A data scientist cover letter highlights your level of education, experience collecting and interpreting large volumes of data, and your ability to create actionable goals from data. It brings the reader's attention to the most compelling parts of your resume and should match your skills with those mentioned in the job posting.

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Data Scientist Cover Letter Template:

Download this template in Microsoft Word format and follow the prompts to make an engaging data scientist cover letter.

Data Scientist Cover Letter Sample:

[Full Name]

[Physical Address]

[Telephone Number]

[Email Address]

Application for the position of Data Scientist.

Dear [Recipient’s Name] ,

I am writing to apply for the data scientist position at [the company’s name] . My excellent analytical and problem-solving skills, advanced knowledge of applied mathematics and statistics, and level of education make me perfect for this position.

I have a [qualification] from [institution] , [location of institution] , and [number of years] years of experience collecting, cleaning, and interpreting data. As per the requirements for the position at [company] , you will notice in my resume that I [mention skills, competencies, or experiences that answer directly to the requirements listed in the data scientist job posting] .

In my previous role at [company] , I [discuss your core responsibilities] . In [year] , I lead a team [talk about a major accomplishment] . During my time at the company, I have learned [what have you learned and how have your skills improved] .

I am thrilled to be applying to [company] because I admire your [name two or three things you know about the company in general that you like] . As the data scientist, my unwavering curiosity, honed creativity, and proficiency with writing algorithms will be of the greatest benefit to [company] .

Please find my resume attached. Feel free to contact me via email or phone call if I can be of any further service.

Thank you for taking the time to consider my application. I look forward to hearing from you.

[Your Name]

How to Write a Data Scientist Cover Letter:

A step-by-step guide on how to write the perfect data scientist cover letter for your next job application.

Address your letter.

Address the hiring manager directly..

Address your data scientist cover letter to the hiring manager or the person who posted the job ad. This will indicate that you have a personal interest in the company.

Avoid using generic greetings.

Using "To whom it may concern" is no longer considered professional. It is better to find out the name of the addressee and use an appropriate title before their name, such as Mr., Ms., or Dr. For example, "Dr. Dodson" or "Ms. Ellis."

Start with a brief introduction.

Make your intentions clear..

In no more than three sentences , tell the person why you are writing and what position you are applying for. Use one sentence to state clearly that you feel your knowledge and experience make you the best person for the position

Highlight your strengths.

Highlight your strongest attributes here. You might want to mention your excellent analytical skills or your advanced knowledge of statistics.

Showcase your qualifications and experience

List your qualifications first..

Write a few sentences on your qualifications, only mentioning those that pertain to the job you are applying for.

Discuss your employment history.

Mention how many years you've worked as a data scientist. Then, bring attention to your resume and discuss those skills you have that match the requirements listed in the job ad. These may include advanced machine learning and programming, experience using data visualization tools and mathematical software, and your understanding of linear algebra.

Discuss your most recent role and responsibilities.

Expand on your current or previous role..

In a few sentences , unpack your current or previous role and how it has prepared you for the job you are applying for.

Highlight your responsibilties.

When you discuss your current or previous role, highlight your key responsibilities and requirements that correspond to the job you are applying for. These may include automating processes, meeting with teams, developing mathematical models, researching solutions, and communicating complex ideas.

Celebrate the organization

Tell them why you want to work there..

Mention two or three things you know about the company you're applying to and why being a data scientist would excite you.

State how you will add value.

State how you will add value to the organization by mentioning, for example, your excellent communication skills, advanced understanding of data querying languages, etc.

End with a call to action.

Provide an opportunity for feedback..

Say that you've attached your resume for their consideration and invite the reader to contact you via email or phone call.

Close your letter.

Thank the reader for their time. Add "sincerely" or an acceptable synonym and sign off with your full name.

What is the purpose of a data scientist cover letter?

The purpose of a data scientist cover letter is to introduce yourself, explain why you've sent a resume, and highlight the most compelling parts of your resume.

How do you write a data scientist cover letter?

• Find out the name of the addressee and address the letter accordingly .
• Write an introduction of a few sentences on why you are writing and why you are the best choice for the position.
• Give a paragraph to your qualifications and experience .
• In another paragraph, write about your current or previous role as a data scientist.
• Use the next few sentences, in a new paragraph, to say why you want to work at the company.
• Then, draw attention to your resume and ask the person to read it .

How long should a data scientist cover letter be?

A data scientist cover letter should be no longer than one page.

How do you start a data scientist cover letter?

Start your data scientist cover letter by telling the person why you are writing and what position you are applying for. Then, in one sentence, explain why you are the best fit for the position by highlighting your strongest attributes.

Who do you address a data scientist cover letter to?

It is best to find out the name of the hiring manager or the name of whoever posted the data scientist job posting and address your letter to them using an appropriate title such as "Ms." or "Dr."

What skills should I mention in my data scientist cover letter?

It is very important that you read the skills requirements listed in the job ad for a data scientist position. You'll want to match your skills with those mentioned in the ad. Some of these may include 5+ years of industry experience, advanced coursework in machine learning, multi-variable calculus, experience using data visualization tools, etc.

Related Articles:

Data engineer resume, data analyst resume, data analyst cover letter, big data engineer resume, data scientist job description.

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How to Write an Effective Data Scientist Cover Letter and Resume

In today’s data-driven world, data scientists are among the most highly respected experts in the tech industry. This interdisciplinary field relies on scientific algorithms, methods, systems, and processes to extract, store, and manipulate structured and unstructured data. These experts are in high demand in the United States and other parts of the world. 

If you are a data scientist who has yet to join the labor market or you’re looking for a career change, you are going to need an excellent cover letter and resume. This is your complete guide to creating a data scientist cover letter and resume for your next job application. It contains everything you need to know including sample cover letters, sample resumes, and a ready-to-use template. 

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Data scientist cover letter guide.

Before you start writing a data scientist cover letter, you need to understand the intention behind a cover letter. Data science cover letters are job application documents consisting of key details explaining why a hiring manager should give you a job. A cover letter supplements your resume by going into more detail about your key qualifications.

Why Does Your Data Scientist Cover Letter Matter? 

There is no doubt that data scientists are currently in high demand. The United States Bureau of Labor Statistics predicts a 22 percent rise in the demand for these experts between 2020 and 2030. To take advantage of this high demand, more people than ever before are learning data science in universities and bootcamps. 

With a professionally written cover letter, you can stand out among hundreds of applicants seeking the same data scientist position. Your cover letter is a gateway to your resume and a perfect opportunity for you to make a good first impression.

Apart from flaunting your technical skills, a data scientist cover letter gives the hiring manager some insight into your personality. It stimulates their interest and encourages them to learn more about you. If your cover letter isn’t good enough, there is a chance that your application will be thrown out before your resume is opened. 

Essential Components of Your Data Scientist Cover Letter

While every data scientist cover letter should be unique and tailored toward the company and job position you are applying for, there are some key components you should always include. These components make up the structure of the cover letter. 

• Contact information. Contact information is key for every type of cover letter, not just data science cover letters. Make sure you add your name, telephone number, email address, professional LinkedIn profile, and GitHub profile. If you don’t have a GitHub link, you should add another link to your portfolio. 
• Introduction. Cover letters should always have an introduction, a body, and a conclusion. You should state your name in your introduction and give the hiring manager some background professional information. Your introduction should be semi-formal with a personal touch. You don’t want to sound mechanical or generic. 
• Professional qualities. The body of your letter should aim to answer some of these questions: Why are you a good fit for this job? What technical and non-technical skills have you acquired over the years and how would they combine to help you excel if you are given a chance? Do you know about data visualization, big data analytics, and business intelligence? What is your data science specialization? 
• Company and job details. In the body, you should mention the name of the company you want to work for and give some details about the job you want. It is also important that you mention the name of the hiring manager for a personal touch. It shows that you’ve done thorough research and know exactly what you’re after. 
• Conclusion. Your conclusion should be compelling and should come with a call to action. First of all, remember to thank the reader for their time. Repeat why you think you will be a good fit for the job and let them know that you will be waiting for their call or email. 

5 Tips for Writing a Strong Data Scientist Cover Letter

Almost every cover letter submitted to the hiring manager will have the same structure and components as yours. You need to go the extra mile to make your cover letter better than everyone else’s. The tips below should help you achieve this goal. 

• Be enthusiastic. If you write your cover letter with enthusiasm, you will show your prospective employers that you are excited about the position and would give it your all if you get the chance. Use words like eager, keen, amazing, and fantastic. 
• Be confident, not arrogant. Hiring managers want to employ data scientists who are confident in their skills. If you are a newbie, you may not be confident. However, if you are an experienced data scientist, you may just be too confident and run the risk of appearing arrogant. Arrogant employees aren’t known to command respect in the workplace. 
• Avoid fluff. In writing, fluff refers to padding up your content with unnecessary filler that adds no value. You should avoid this in your letter by sticking to the most important points and staying away from repetition except for necessary emphasis. 
• Don’t list outdated skills or tools. The data industry is one of the most rapidly evolving industries in the world today. Hiring managers are looking for people with in-demand and up-to-date skills. Cover letters are already very short, so don’t waste space by mentioning outdated technologies or tools that don’t improve your candidacy.
• Avoid negativity. You may be tempted to complain about a previous employer or mention a negative experience you had in the industry. However, your cover letter isn’t the place to air any grievances. Focus exclusively on what makes you a good candidate for the job at hand.

Data Scientist Cover Letter Example

You should keep your cover letter short and straightforward. The sample below is a guide to help you write a compelling data science cover letter. Feel free to make yours longer if you have enough meaningful information to add. 

Data Scientist Cover Letter Sample

November 20, 2021

Miss. Anita Johnson 

[email protected]

Hiring Manager

Data Science Corporation

No 5 Hive Street, Montgomery Street, Silicon Valley, CA, USA 09857

Dear Miss Johnson, 

My name is Jane Doe and I am excited to apply for this role. With my vast experience in data modeling, data visualization, statistics, and data analytics, I am confident that I am the best fit for this job. 

I have a Bachelor’s and Master’s Degree in Data Science but my goal is to continue pursuing professional development. Apart from my technical skills and experience, I consider myself a leader with exceptional communication, critical thinking, problem-solving, and analytical skills. 

I believe that machine learning and artificial intelligence are the future of data science and I have dedicated time to explore this field. I currently have a machine learning and artificial intelligence certification along with data science certifications. 

If you give me a chance, I will play my part in ensuring that Data Science Corporation makes the best data-driven decisions. Thank you for your time and I look forward to reading your reply soon.

Sincerely, Jane Doe 

Data Scientist Resume Guide

A data scientist’s resume is a formal job application document that showcases your education, skills, professional experience, and other related qualifications. You can impress hiring managers with a high-quality resume and increase your chances of getting invited for a job interview.

Why Does Your Data Scientist Resume Matter?

Your data science resume matters because it helps the hiring manager determine if you are a good fit for the position. The structure, style, and occupational details on your resume will go a long way in determining if you deserve the first interview. If your resume is not properly crafted, you may appear less qualified than you are and lose a once-in-a-lifetime opportunity. 

To make an even more compelling data science resume, you need to attach it to a cover letter. Combined, both documents must reflect your strengths as a data scientist. You can use it to show the hiring manager that you are excited about the job offer and determined to make the best out of it if you are offered the position. 

Essential Components of Your Data Scientist Resume

The following components of a data scientist resume are must-have features that help structure and bulk up the document. You’d better include these on your resume if you want to be considered for an interview. 

• Contact information. Your contact information is vital because it is the first thing a hiring manager will look for if they want to invite you for an interview. This section should include your telephone number, email address, LinkedIn profile, and GitHub link if you have one. 
• Personal statement. Your personal statement is an introduction to your resume. It is also called an objective statement or professional summary. Think of your personal statement as your sales pitch for your services. Mention the skills you feel qualify you for the data science position. It can be a combination of technical and non-technical skills. 
• Education. List your university degrees and other post-secondary education. For each entry, include the name of the school, the name of the degree, and the dates you attended. 
• Professional experience. A vital part of your resume is your work history. Hiring managers in the data science industry prefer to employ people who already have professional working experience. If you are applying for an entry-level role, add any internships or apprenticeships that you have done.
• Skills and certifications. There should be sections in your resume dedicated to your technical and non-technical skills. Make sure you list the most relevant skills first. You also need to list your data science certifications. 

5 Tips for Writing a Strong Data Scientist Resume

To ensure that your resume stands out from every other one submitted to the hiring manager, follow these five tips: 

• Be honest. Don’t try to get an edge by lying about your job experience, skills, education, or anything else. Even if you get away with lying and get hired, the company will eventually find out when you are unable to do what you said you could.
• Include quantifiable results. As a data scientist, you should be both good with and passionate about numbers. To convey this in your resume, make sure that you include achievements from previous roles measured in quantitative terms. State by what percentage your work helped improve a company’s bottom line or how you helped a firm predict sales with a certain accuracy rate.
• Add high-demand skills. Start by listing the most in-demand skills first. High-demand programming languages include Python, SQL, and R. Tools that any data science resume should feature are Hadoop, Spark, Tableau, and MongoDB. You should also mention your machine learning and artificial intelligence skills. 
• Don’t be flashy. There is nothing wrong with adding some color to your resume, but the best ones stick to black and white. If you must add color, it should be restricted to the headings and divider lines. Your goal should be to keep your resume simple and free of clutter. Use professional font style and size. 
• Proofread. When you finish writing your resume, you need to proofread it thoroughly to make sure there are no grammatical errors. No hiring manager will take you seriously if your resume is full of typos and other grammatical mistakes. 

Data Scientist Resume Examples

The samples below should give you a clearer picture of what a data scientist’s resume should look like. One is a complete resume sample and the other one is a template that you can fill in with your own information. 

"Career Karma entered my life when I needed it most and quickly helped me match with a bootcamp. Two months after graduating, I found my dream job that aligned with my values and goals in life!"

Venus, Software Engineer at Rockbot

Data Scientist Resume Sample

Jane Doe 

(+1) 8407-5213

[email protected] 

GitHub.danejoe.com

Personal Statement 

Over the last five years, I have built a solid reputation as a data scientist who specializes in machine learning, deep learning, and artificial intelligence. Apache Hadoop is my go-to software for distributed data storage and processing. I also have excellent leadership skills due to my extensive experience in team building and management. 

Columbia University, New York City, NY

Master’s in Computer Science – Machine Learning

2013 – 2015

University of California, Los Angeles, CA

Bachelor of Science in Data Science 

2008 – 2011

Work Experience

Data Scientist

The Data Science Republic, San Diego, CA

2015 – Present 

• Supervised data migration from traditional to cloud-based database management systems with less than 1% obstruction or data loss 
• Developed software for data recovery   

Remote Data Analyst

Computer Management Systems, New York City, NY 

2010 – 2015

• Collected, stored, and analyzed data which was used to offer actionable insights 
• Designed a data management software that improved overall DBMS performance by 20%

Technical Skills

Hadoop 

Spark 

Sybase 

JavaScript 

Non-technical Skills

Time management 

Communication 

Problem-solving 

Working under pressure 

Collaboration

Conflict resolution 

Adaptability 

Certifications  

IBM Data Science Professional Certificate 

Google Data Machine Learning

SAS Certified Data Scientist

Data Science Council of America (DASCA) Senior Data Scientist (SDS)

MongoDB Certified Developer

References available on request 

Sample Data Scientist Resume Template

[Full name]

[Phone number]

[Email address]

[Portfolio link]

[LinkedIn profile]

Personal Statement [One or two sentences about your career goals and what you’re seeking from the job you’re applying for]

Education [School and location] [Degree and major] [Dates]

[School and location] [Degree and major] [Dates]

Work Experience [Position] [Company and location] [Dates]

• [Responsibility]

[Position] [Company and location] [Dates]

Technical Skills [Skill] [Skill] [Skill] [Skill] Non-technical Skills [Skill] [Skill] [Skill] [Skill]

Certifications [Certification] [Certification] [Certification]

Data Scientist Resume Template Resources

• Enhancv . Since 2014, Enhancv has built a solid reputation as one of the best places to find customizable resume templates for data science and many other professions. 
• Monster . Monster is a job resource website that provides data science job listings as well as cover letter and resume templates. All templates can be downloaded for free. 
• Beamjobs . Beamjobs has great resume samples and templates based on over 1,000 real-world data science resumes. There are templates for entry-level, intermediate, and senior data scientists. 

Importance of a Data Scientist Cover Letter and Resume

Crafting an impressive cover letter and resume is vital for people seeking data science positions. These documents might just be what stands between you and your dream job. Before an interview is granted, hiring managers use cover letters and resumes to gauge your abilities and see if you are a good fit for the company.

Data Science Cover Letter and Resume FAQ

Yes, a data science cover letter is necessary for anyone who wants to apply for a job in the tech industry. Big companies have HR departments that oversee recruitment. If you don’t submit a professional cover letter, your application won’t probably be considered.

A data science resume should contain your business-oriented achievements, business experiences, education history, skills, and successful data science projects. It should speak loudly about your qualifications and leave the hiring manager curious to meet you in person. If possible, include any skills or technologies listed in the job description.

Start with the most relevant and in-demand data science skills you have. After listing these skills, you can proceed to other technical skills and then move to soft skills like communication, problem-solving, and similar traits.

Yes, you do need a cover letter and a resume if you are applying for a position. However, you should not combine them into one document. While they are both parts of the job application process, they both serve different purposes and should not be condensed into a single document.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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Data Science Cover Letter

By thinkful.

After completing your education in data science, chances are you’ll be looking to land a job. As a new data scientist, you understand how to analyze big data for understanding, prediction, intervention, and exploration. You also know that you’ll have to coordinate and work together with different teams in the company to get results. But is that enough to get a job?

Usually new grads spend a lot of time building a strong resume, and preparing a portfolio that highlights all of their new skills. But unfortunately that’s usually not enough to land a job in data science. Your cover letter is what actually introduces you and your skills to the company. It’s through the cover letter that you can demonstrate your knowledge and catch the attention of your potential employer.

A cover letter is sometimes even more important than a resume or CV because it conveys your personality and communication skills. So we’re going to guide you through the process of creating a strong data science cover letter. We’ll discuss what information to include and what to leave out to give you the best shot at getting hired.

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Writing Style

Even in a highly technical field like data science , the writing style of your cover letter is extremely important. The way you write your cover letter should be professional while also revealing your personality. Keep the style of your writing consistent from greeting to sign-off. Most importantly, your writing should reflect your communication skills and your ability to express your thoughts - two soft skills that are vital in any data science role.

After you’ve taken a stab at writing your cover letter, take a look at the below list of writing tips before clicking Send.

• Make sure your letter is an appropriate length: not too long or too short. A short letter won’t do justice to your full range of skills and experience. If it’s too long, you’ll lose the attention of your hiring manager.
• Two to four short paragraphs will do. Each paragraph should have different content, but it should all be relevant to a data science position. Take out repeated statements and trim down your writing to the most relevant points.
• Add transition statements so each paragraph flows together.  Make sure every paragraph supports your end goal:  to land the job. For example, you might begin the letter by writing about your interest in data science and the reason behind your interest. This idea could then be related to your next short paragraph, which could be about your specific credentials.
• After a short paragraph about your qualifications, mention any professional experience in the field.
• Make sure to discuss all of your skills, and why they’re relevant for a data science role. For example, you could describe your efficiency in optimizing algorithms and handling the tradeoff between speed and accuracy.

Once you’re happy with your letter, do a final check for spelling and grammatical errors. After putting all that time into writing the perfect letter, the last thing you need is a busy recruiter overlooking your application because of a typo.

What Information Should You Include?

Your primary goal should be to explain why you’re the ideal candidate for the job. To do this, you need to frame your letter by summing up all your skills and experiences that would help you to excel in the role.

• Elaborate on your educational qualifications along with your data science work experience.
• Mention the specific aspects of the role or company that attracted you to the job.
• Mention the specific skills that are mentioned in the job description. Remember, this is your chance to show the company why they should interview you instead of another qualified data science candidate.
• Begin your paragraphs with strong, direct statements. For example, you can begin with something like, “I have three years of experience as a data scientist and recently worked on project X and project Y. This experience has taught me A and B skills that could add value to your company.”
• Be sure to include your knowledge about the company. You should genuinely believe that the needs of the company and your skills would merge to produce beneficial results for both of you.
• Don’t forget to include your recent projects and achievements.
• As a data scientist you should mention that you don’t shy away from a challenge and value consistent learning.
• Lastly, at the end of your cover letter you’ll want to encourage the employer to get in contact with you to schedule an interview.

Describe Your Data Science Skills

This point is so important that it’s worth repeating. Elaborate on all of your relevant skills, even if they’re also listed in your resume. Along with technical skills like programming and creating data, you should also mention the many soft skills that make for a great data scientist. For example, in addition to mentioning your expertise at helping business stakeholders meet their goals through data manipulation, you should also mention your teamwork and communication skills.

Now that you have a better understanding of what goes into a data science cover letter, let’s take a look at a sample.

Cover Letter Sample

Here’s an example of a thoughtful cover letter written by an aspiring data scientist.

Dear Mr. XYZ, (the hiring manager),

I am Mr. ABC, and I am here to apply for the open position as a data scientist at PQR Tech--one of the most renowned organizations in the field of advanced technology. I have three years of professional experience as a data scientist, and recently worked on project X and project Y. This experience has taught me valuable skills that could benefit PQR Tech.

I have always been deeply interested in statistics, analytics, and mathematics, which led me to study data science. As a data science student, I was required to be skilled at understanding algorithms, MatLab toolkit, and be well-versed with SQL and applied statistics. Immediately after completing my education, I joined Jenkins Tech. It was there where I first used my academic knowledge and handled multiple projects independently.

The requirements of your job profile perfectly match my experience and skill set in mathematics. Besides my academic strength, my three years at Jenkins has sharpened my ability to communicate with people and various team leaders. This practical knowledge has also allowed me to understand what it takes to achieve big company goals. With my latest success in project X and Y, I am confident that I will be a valuable addition to your firm.

I would like to express my gratitude for your time and consideration. I look forward to further discussing working with your prestigious organization soon. I hope that my education and experience will be able to serve your company well.

Job Availability

When you’ve completed your education to become a data scientist , you’ll be well-versed in areas like SQL, command line and cloud tool.  Once you’ve got that under your belt, and understand how to write a great cover letter and resume, chances are you’ll be able to land a job you love. Data science positions are expected to rise in demand by 27.9% by 2026, as reported by BLS. With that being said, there will be plenty of job openings for you.

Get Hired As A Data Scientist

If you’d like more support on your journey to becoming a successful data scientist, Thinkful’s team is here to support you. From one-on-one mentorship and a committed support team, to dozens of useful data science articles , we’ve got you covered.

Launch Your Data Science Career

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• What is Data Science?
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• Data Science Terms
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• Data Scientist vs Business Analyst
• Data Science vs Computer Science
• Data Scientist vs Data Scientist
• Data Scientist vs Data Engineer
• Data Science vs Software Engineering
• Data Scientist Cover Letter
• Data Scientist Entry Level Jobs
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• Data Scientist Interview Questions
• Data Scientist Job Search
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Data Scientist Cover Letter Example

Get closer to landing a topnotch Data Scientist job with this expertly written cover letter sample. Use this cost-free example as it is or alter it in any way using our professional cover letter builder.

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Data Scientist Cover Letter Example (Full Text Version)

Emily saavedra.

Dear Hiring Manager,

I am writing to express my interest in the Data Scientist position at your esteemed company. With over three years of experience in the field, I have developed a strong understanding of key concepts and principles essential for successful execution of the role.

In my current role as a Data Scientist, I have honed my skills in data analysis, forecasting models, report generation, and data management. My attention to detail, accuracy, and ability to thrive in a fast-paced environment have been instrumental in my professional growth.

In addition to my practical experience, I hold a degree in Mathematics and Statistics and possess a Professional Certificate in Data Analysis from the Chartered Institute for IT. My analytical skills, multitasking abilities, and time management skills further demonstrate my commitment to a data-driven approach.

I am highly passionate about my work and believe that my determination and results-oriented mindset make me a valuable asset to ASRTech, Inc. Please find more details about my qualifications in the attached resume.

I look forward to the opportunity to discuss how my skills and experience align with the needs of your team. Thank you for considering my application.

Emily Saavedra

Data Scientist

Adept at statistics, mathematics and computer science, data scientists are professionals who collect and analyze high volumes of structured and unstructured data. They examine and experiment in order to find trends and create actionable plans for businesses. The role of a data scientist is to a great extent about the ability to communicate results after a thorough data evaluation, while employing deep learning models. A data scientist's annual mean wage can, according to the U.S. Bureau of Labor Statistics, reach as much as $ 133,900.

Milan Šaržík, CPRW

Milan’s work-life has been centered around job search for the past three years. He is a Certified Professional Résumé Writer (CPRW™) as well as an active member of the Professional Association of Résumé Writers & Careers Coaches (PARWCC™). Milan holds a record for creating the most career document samples for our help center – until today, he has written more than 500 resumes and cover letters for positions across various industries. On top of that, Milan has completed studies at multiple well-known institutions, including Harvard University, University of Glasgow, and Frankfurt School of Finance and Management.

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3 Data Scientist Job Description Templates for 2024

• Data Scientist
• Senior Data Scientist
• NLP Data Scientist
• Write a Data Scientist Job Description

The profession of data science is continuing to grow and is a very in-demand role. Data scientists typically work for corporations across all industries to provide meaningful insights about how to improve business operations.

Finding and hiring an exceptional data scientist for your company or organization can be a time-consuming and arduous process. But we’ll give you some great examples and expert guidance to help create the best data scientist job description that will attract the most suitable candidates for you.

Data Scientist Job Description Example

USE THIS TEMPLATE

Microsoft Word

Google Docs

Senior Data Scientist Job Description Example

NLP Data Scientist Job Description Example

Write an Effective Data Scientist Job Description 

Crafting an effective and well-written job description is not always easy. A data scientist role is especially difficult because it is a highly technical role that encompasses a broad range of skills and varies greatly from company to company. The biggest challenge you will have is  keeping it from being too lengthy  and  staying focused on the most important things  your data scientist needs to know and what they will need to do.

Condense your job description down to the vital information

A data scientist must perform many complex tasks simultaneously. They are required to assess large amounts of data, quickly and efficiently identify essential data, and distill it down to the most valuable information. Likewise, your job description must be distilled to the essentials—nothing more. 

Ensuring your potential candidates will read the entire job description is a major objective you must keep in mind as you write. The most essential thing you need to do is keep your job description brief and concise. Listing all the pertinent job requirements for a data scientist role without creating a lengthy document can be difficult, but make a concerted effort to include the key info and “kill your darlings” by eliminating the unnecessary. 

Another major objective to remember is that you’re selling the job and your organization. You are trying to attract as many qualified applicants as possible. The best way to demonstrate what a job entails is by giving examples of issues a data scientist in your company will face. For instance,

• Provide particulars of a problem they’ll need to fix (developing custom data models and algorithms to apply to data sets),
• Discuss a gap they’ll fill (improvements to data quality and pricing model accuracy), or
• State why you’re specifically hiring for the role (to institute a data science center of excellence). 

Returning full circle, George Mason University’s writing center stresses the importance of  condensing your writing . According to the experts, writing concisely means that you “write what you mean—nothing more and nothing less.” When you create a data scientist job description, consider each word choice and look for extra words you can remove without changing the meaning. Remove filler words and extraneous language.

Write to encourage diverse candidates

Focus your requirements on the critical and unique needs of your company, eliminating common data science skills or traits, such as “strong data analysis skills.” This fluff clogs up your job description, not to mention that listing too many requirements tends to discourage diverse candidates from applying, especially in STEM. 

Writing a completely unbiased data scientist job description isn’t always easy despite your best efforts. It takes a conscious effort to use words and phrases that will encourage diverse populations to apply. MIT’s “ Ensuring Bias-Free Job Postings ” gives a high-level overview of the issue and has some pointers to help you avoid pitfalls that incidentally stereotype or alienate job seekers.

Edit and revise for content and grammar

The final and most important step to creating a great job description is editing and revising. Invite people in the data science function to review your work as any additional review is beneficial. Seek out constructive criticism, and be willing to make changes if appropriate.   

The worst thing you can do is submit a job description that has poor word choice, grammatical errors, or even worse, an inaccurate salary range with one too many zeros. Take your time, and when you’re done, you can be confident that you’ve published a high-quality listing that will attract the best talent for your data scientist role.

Start by Framing Your Data Scientist Job Description

We’ve shown you the examples and stressed the importance of writing your job description well, but we get that those things can seem a bit arbitrary when you’re staring at a blinking cursor on a blank screen. 

Check out the following outline that covers all the sections for any data scientist job description ; then, all you need to do is fill it in, drawing upon inspiration from the examples and how-to guide above.

Job details

This section is where you briefly introduce the company and the open position. You should leverage this section to immediately grab the reader’s attention. Offer candidates a taste of why your company is a great place to work. Then, in a couple of sentences, give them a quick blurb on the what and why of your data scientist role to encourage job seekers to read the rest of your job description.

About the company

This section of your job description should contain additional information about your company. A data scientist will be interested in knowing a company’s strategy regarding data analysis, the usage of big data, and how the company leverages information technology. Cover these items, or anything directly related, in a sentence or two, noting the role of the data scientist.

What you’ll be doing

You may also call this section the “Roles” or  “Responsibilities” or “Requirements.” This section should be a bulleted list of the key tasks performed in the job role. Don’t make it too long. Ensure you include anything unique to or especially cool about the data scientist role in your company. Emphasize anything critical to the success of the position. As with any business writing, always use active verbs, avoid jargon and filler words, and be as specific as possible.

• Collect data from established channels and through means such as analyzing business results or the creation of new studies.
• Create new, experimental frameworks to collect data, and build tools to automate data collection.
• Carry out preprocessing (structured and unstructured data) cleansing, and validating the integrity of data to be used for analysis.
• Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
• Correlate similar data to find actionable information and present results concisely.

Qualifications

This section is the make-or-break section. It quickly tells candidates whether they are a good fit for the job. Craft this section carefully. Always include anything critical and must-haves for the position. A good job description will list education, experience level, certifications, and any special requirements. 

Technical qualifications should always be described as specifically and objectively as possible. Consider whether you need to include soft skills at all. Perhaps you can discern that in an applicant’s  data scientist resume  or  data scientist cover letter , saving room to list job-specific qualifications, such as text classification topic mining or speech enhancement.  

As you know, data science  can  include  a lot  of very technical requirements. Focus on the job’s critical aspects, but avoid having too long a list. If your company has a lot of unique or extraordinary requirements, then use most of this section to cover that. As with every part of your job description, be brief, specific, and clear.

• Master’s or Ph.D. in statistics, mathematics, computer science, artificial intelligence, machine learning, or another quantitative field
• 5+ years of professional experience as a data scientist or in a related role
• 3+ years of work experience in voice-related projects
• 3+ years of work experience in software development
• Experience establishing supervised and unsupervised learning Client/NLP models, including data cleaning, data analytics, feature creation, model selection and ensemble methods, performance metrics, and visualization
• Knowledgeable of signal processing techniques, including adaptive filtering, filter banks and wavelet processing, speech analysis, and synthesis, and speech and audio coding
• Expertise in text classification topic mining, speech enhancement, and speech/audio coding and compression

Nice-to-have

If you have a lot of other skills that you are looking for, but they are not deal-breakers, then you may include these as a subsection to your qualifications. List all the things you consider to be key differentiators for candidates. Again, try not to list too many because this will discourage some candidates.

The benefits section is a brief but standard item to include in your job description . Where you place it in the overall format is not so important, though it shouldn’t be the first item. If your company has outstanding benefits and/or something extraordinary to offer, such as an onsite gym or an unlimited vacation policy, then you may want to place this section earlier to snag the reader’s attention. 

On the Job: The Responsibilities of Data Scientists

Data scientists are researchers and masters of quantitative analysis. They investigate extremely large volumes of data and solve business challenges using data. Advanced data science involves identifying relevant data, detecting patterns and trends, and translating them into viable business plans and strategies. A data scientist must rapidly process and transform large data sets into understandable and usable business information.

Below are some sample roles a data science professional might fulfill. A single scientist may not fulfill all these roles, but this will give you are good list to choose from and add to based on your company’s specific needs.

Data acquisition

• Ask the right questions to drive the discovery process in the right direction.
• Perform initial data investigation and exploratory data analysis.
• Examine internal and external sources of data and detect important patterns and trends.
• Determine processing methodologies to best transform data into usable business intelligence.
• Think critically about how data is used, and assess and quantify strengths, weaknesses, and opportunities posed by data sources and processing.
• Continually seek new data sources, ways to improve data processing, and new technologies to better utilize data.

Problem solving

• Study business challenges, research relevant data processing techniques, and determine solutions through data analysis.
• Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence.
• Process and clean data to create useful databases and information.
• Effectively integrate and store data into databases and business intelligence systems.
• Identify problems in data acquisition, processing, and utilization; then, assess and resolve them.
• Continually partner with business and data analysts to review data processing and make recommendations for improvements.

Communication and reporting

• Partner with technical and data analysis teams to understand business processes and data sets to create documentation and presentations that translate data into coherent business information.
• Present high-level results and recommendations to executive-level stakeholders.
• Assess business feedback and make appropriate adjustments to data processing and analysis procedures.
• Facilitate business communication and reporting for internal and external stakeholders.

• Provide direct expertise and leadership to business owners on the analysis and understanding of data utilization in business processes.
• Consult with and provide direction to stakeholders (internal and external) regarding best practices and effective data use to improve business processes.
• Lead teams, organizations, and programs to productively leverage data gathering and analysis to successfully complete tasks, initiatives, and projects.
• Research and stay abreast of the current state of data science.
• Consistently spend time studying, completing professional training, and maintaining all certifications.
• Engage with business units and regularly assess how they are utilizing data, information, and reporting tools to ensure that business strategies and objectives are achieved.
• Spearhead ongoing training and technical education for data analysis professionals and business process owners to enhance and evolve the organization’s data analysis capabilities.

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Data Scientist Salary and Job Description 2024

In today's fast-paced business world, data scientists play a vital role in transforming complex data into actionable insights. Their work guides strategy and decision-making at the highest levels of organizations. It's no surprise, then, that the data scientist salary for skilled professionals continues to soar. In fact, employment of data scientists is projected to grow by an impressive 35% from 2022 to 2032, according to the US Bureau of Labor Statistics . This rapid growth reflects the critical importance of their work.

Data scientists' expertise in machine learning and analytics and their ability to derive meaning from data set them apart. With a unique blend of technical skills and business acumen, they help companies gain a competitive edge in an increasingly data-driven landscape.

As companies collect and analyze ever-increasing amounts of user data, the role of the data scientist has never been more important. Those who can uncover valuable insights while navigating the complexities of data privacy and ethics will be in high demand for years to come. By investing in the right data analyst skills today , aspiring data scientists can position themselves for a bright and impactful career in this essential field.

What does a data scientist do?

On a typical day, a data scientist collects data from various sources, cleans it, and preps it for analysis. They spend a good chunk of time exploring datasets to uncover trends and insights that can help address business challenges. For instance, a data scientist at a retail company might dig into sales data to figure out the best pricing strategies. Another key part of the job is sharing findings through engaging data visualizations and presentations.

To succeed in this role, data scientists need a mix of technical chops and people skills. On the technical side, that means things like:

• Machine learning
• Statistical analysis
• Programming ( Python , R )

Alongside these technical skills, data scientists also have to be great communicators who can partner with teams across the company to tackle data-related problems. And since the field is always evolving, continuous learning is a must. While many data scientists have degrees in computer science or statistics, alternatives like the Data Scientist in Python path from Dataquest can also equip you with the skills you need to break into this challenging but lucrative field.

Salary and career outlook

Want to know what you can expect to earn as a data scientist? According to Glassdoor , the current median annual wage for data scientists in the U.S. is \$121,525, with their base pay ranging from \$93,000 to \$159,000 per year.

Factors that impact data scientist salaries

As noted by Caltech , several factors can impact how much you'll earn as a data scientist:

• Where you live: Data scientists in certain cities, like Palo Alto (\$156,923/year) and San Francisco (\$139,757/year), tend to command higher salaries.
• The industry you work in: Real Estate and Information Technology companies often pay their data scientists more.
• Your level of experience: Entry-level data scientists usually start around \$85,052 * per year, while senior data scientists can make an average of \$167,572 * per year.

So what does a typical data science career path look like? Most folks start out in entry-level roles, then work their way up to senior positions like data science manager or director of data science. The key is to keep learning and developing your skills along the way. With the right knowledge and experience under your belt, the sky's the limit!

Entry-level data scientist salary and job description

An entry-level data scientist position provides an exciting launchpad into the rapidly growing field of data science. In this role, you'll have the opportunity to work with large datasets, uncover meaningful insights, and directly impact an organization's strategy from day one.

Common job titles:

• Junior Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Intelligence Analyst
• Quantitative Analyst

As an entry-level data scientist, you'll immerse yourself in collecting, analyzing, and interpreting complex data to help drive informed business decisions. It's a role tailor-made for those who love solving puzzles, have a knack for spotting patterns, and thrive on turning raw numbers into actionable insights.

On a day-to-day basis, you'll be responsible for gathering and cleaning large datasets , then employing statistical analysis and machine learning techniques to develop predictive models . You'll collaborate closely with cross-functional teams, helping them understand your findings and how they can leverage data to optimize their strategies. Commonly used tools include Python , R , SQL , Tableau , and libraries like TensorFlow or scikit-learn .

Most entry-level positions look for candidates with a Bachelor's degree in a field like Computer Science, Statistics or related disciplines. Hiring managers also love to see hands-on experience through internships, projects or online courses, as it shows you can apply your skills in real-world scenarios.

Entry-Level Data Scientist Salary: According to Glassdoor , the typical entry-level data scientist salary ranges from \$87,000 to \$143,000 annually.

Key responsibilities:

• Collecting and cleaning large datasets
• Developing predictive models using machine learning algorithms
• Identifying patterns and trends from data analysis
• Presenting findings clearly to stakeholders
• Collaborating with cross-functional teams on projects

Must-have skills:

• Proficiency in Python or R programming languages
• Strong understanding of statistical analysis methods
• Experience with SQL for database management
• Aptitude for machine learning techniques

When applying for entry-level data scientist roles , be sure to highlight your relevant coursework, internships, and projects in both your resume and cover letter. During interviews, prepare to discuss your experience working with data, share examples of how you've solved problems with data in the past, and don't hesitate to show your enthusiasm for continued learning in the field.

If you're analytically inclined, endlessly curious, and ready to harness the power of data to uncover valuable insights that shape organizational strategies, a career as an entry-level data scientist could be your ideal launchpad into this dynamic, fast-growing field. Data science is ripe with potential for driven individuals like you.

Mid-level data scientist salary and job description

A mid-level data scientist role offers an exciting chance to tackle real-world challenges and shape business strategy through data-driven insights. It's a key position for translating complex data into actionable information that drives organizational success.

• Data Scientist II
• Machine Learning Specialist
• Business Intelligence Engineer
• Data Analytics Consultant
• Applied Data Scientist

As a mid-level data scientist, you'll apply your expertise to understand business needs and deliver data-based solutions. It's a role that combines technical skills with the opportunity to have a real impact on decision-making.

You'll be responsible for working with large datasets, using data to predict future trends, and communicating your findings clearly to colleagues and stakeholders. The ability to turn complex results into meaningful, actionable insights is what makes this position so rewarding.

On a day-to-day basis, you'll collect, clean, and analyze data using tools like Python, R, SQL, and machine learning frameworks such as TensorFlow. You'll collaborate closely with teams across the company to build models that uncover trends and opportunities.

To succeed in this role, you'll need strong analytical abilities, proficiency in programming languages like Python or R, and experience with data visualization tools such as Tableau or Power BI. While a master's degree in a related field is often preferred, it's not always mandatory if you have relevant experience or a portfolio of successful projects.

Mid-Level Data Scientist Salary: According to Glassdoor , the typical mid-level data scientist's salary falls between \$135,000 and \$200,000 annually.

• Gathering and cleaning large datasets for analysis
• Using data to predict future trends
• Communicating complex findings clearly to non-technical audiences
• Partnering with different teams on data projects
• Keeping up with industry trends and new technologies
• Expertise in Python or R programming
• Solid understanding of machine learning techniques
• Proficiency with SQL for managing databases
• Skill in data visualization using tools like Tableau or Power BI
• Excellent communication abilities for effectively sharing insights

When applying for mid-level data scientist positions , be sure to showcase your technical capabilities, past experience, and standout projects. Highlight your skills in tools like Python, SQL, and Tableau. And get ready to discuss how you've applied your abilities to make an impact in previous roles.

In short, working as a mid-level data scientist is both challenging and fulfilling. You'll have the chance to shape business strategy and drive success through the power of data. And with demand growing, this role provides great potential for career growth.

Senior data scientist salary and job description

Senior data scientists play a pivotal role in leveraging data and AI to drive business innovation and decision-making. They lead data science initiatives, optimize machine learning models, and collaborate with cross-functional teams to deploy models that drive business value.

• Lead Data Scientist
• Principal Data Scientist
• Senior Machine Learning Engineer
• Data Science Manager
• AI Research Scientist

As a senior data scientist, you'll lead the strategic use of data to address complex business challenges and drive innovation. This role demands a deep understanding of both data analytics and business strategy, empowering you to mentor junior team members and shape the future of your organization's data-driven decision-making processes.

In their daily work, senior data scientists manage end-to-end projects, from gathering requirements to deploying machine learning models. They use tools like Python, R, SQL, TensorFlow, and big data technologies such as Hadoop and Spark. Key responsibilities include optimizing algorithms for better performance, ensuring model reliability through MLOps practices, and maintaining data privacy standards.

Educational qualifications often include a master's or PhD in computer science, statistics, or related fields. Hands-on experience with large-scale projects is highly beneficial. Senior roles typically require at least 5-7 years of experience in data science or related domains.

Senior Data Scientist Salary: According to Glassdoor , the typical senior data scientist's salary ranges from \$189,000 to \$286,000 annually.

• Leading the development and optimization of machine learning models
• Conducting thorough data analysis to extract actionable insights
• Collaborating with engineering teams to deploy models into production environments
• Mentoring junior team members and fostering a culture of continuous learning
• Ensuring adherence to ethical standards and data privacy regulations
• Advanced programming skills (Python/R)
• MLOps proficiency for model deployment
• Keen understanding of statistical analysis methods

When applying for senior data scientist roles , highlight your most impactful projects, ability to lead initiatives, and cross-functional collaboration skills. Prepare to discuss how you've optimized models, deployed solutions to production, and mentored junior colleagues. Ensure you prepare for the data science job interview to boost your confidence.

Senior data scientists are instrumental in guiding organizations to make data-informed decisions. If you're passionate about leveraging data and AI to solve complex problems, this could be the ideal role for you. With the right skills and experience, you can make a meaningful impact while enjoying strong career prospects in this high-growth field.

How to Find and Apply for Jobs in Data Science

Landing a job in this competitive field requires a targeted, proactive approach. Here are some proven strategies to help you effectively navigate the job search process and increase your chances of success:

• Optimize your online presence : Ensure your LinkedIn profile is up-to-date, professional, and highlights your data science skills, projects, and achievements. Actively participate in relevant online communities like Kaggle and Data Science Stack Exchange , sharing your knowledge and insights through thoughtful posts and discussions.
• Tap into your network : Stay connected with former colleagues, classmates, and mentors, informing them of your job search. Utilize their potential leads and knowledge about vacancies that fit your skill set.
• Engage with the data science community : Participate in industry conferences like ODSC East , as well as workshops and meetups, both local and virtual. These events are great for networking and can lead to job opportunities.
• Contribute to data science projects : Join data science projects on platforms like Kaggle competitions or contribute to open-source projects on GitHub . This showcases your enthusiasm, technical ability, and teamwork.
• Practice for technical interviews : Data science interviews often include technical assessments, coding challenges, and in-depth discussions about your approach to problem-solving. Ensure you sharpen your technical skills and be prepared to articulate your thought process on problem-solving.
• Customize your application : Tailor your resume, cover letter, and portfolio for each application, emphasizing the qualifications and experiences that are most relevant to the job. Consider using a data science-specific resume template to ensure your application stands out.

Landing your dream data science job requires a combination of technical expertise, strategic networking, and effective self-promotion. By continually expanding your skillset, staying engaged with the data science community, and proactively pursuing opportunities to showcase your talents, you'll be well-positioned to secure the role you've been working towards.

Becoming a successful data scientist is all about playing to your strengths. It's a career that rewards certain qualities like a knack for analysis, an insatiable curiosity, the ability to communicate complex ideas, and of course, serious tech skills in programming and statistics. Put those together and you've got a winning formula for turning raw data into game-changing business insights.

So what's the roadmap look like for aspiring data scientists? It depends where you're starting from:

• Beginners: Focus on building a rock-solid foundation in math, stats, and coding. Get your hands dirty with entry-level data science projects to gain practical experience and start showcasing your skills.
• Intermediates: Level up with real-world projects in areas like predictive modeling, data viz, and NLP. Consider earning certifications in hot topics like MLOps and data ethics to stand out.
• Advanced learners: Tackle cutting-edge applications like recommender systems and sentiment analysis. Contributing to open source projects is a great way to stay on the pulse of industry trends.

No matter where you're at, hands-on projects are absolutely essential . They solidify your understanding, give you practical experience, and prove to employers that you've got what it takes to deliver results in a data science role.

If you're looking for a structured learning path, Dataquest's Data Scientist in Python course guides you through the key skills you need, with plenty of hands-on projects along the way. Plugging into the Dataquest community is also smart for getting support and finding collaborators.

One last thing to keep in mind: the world of data science never stands still. To thrive, you need to be committed to continuous learning, always reassessing your skills and goals to stay aligned with where the industry is heading.

But if you've got the right mix of traits and a genuine passion for the work, you're well on your way to making your mark as a data scientist.

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Data Science Intern Cover Letter Example

Cover letter examples, cover letter guidelines, how to format an data science intern cover letter, cover letter header, cover letter header examples for data science intern, how to make your cover letter header stand out:, cover letter greeting, cover letter greeting examples for data science intern, best cover letter greetings:, cover letter introduction, cover letter intro examples for data science intern, how to make your cover letter intro stand out:, cover letter body, cover letter body examples for data science intern, how to make your cover letter body stand out:, cover letter closing, cover letter closing paragraph examples for data science intern, how to close your cover letter in a memorable way:, pair your cover letter with a foundational resume, key cover letter faqs for data science intern.

Start your Data Science Intern cover letter by addressing the hiring manager directly, if possible. Then, introduce yourself and state the position you're applying for. Express your enthusiasm for the role and briefly mention how your skills, education, or experience align with the job requirements. For instance, you could say, "As a third-year Computer Science student with a focus on data analysis and machine learning, I am excited about the Data Science Intern position at XYZ Company. I believe my academic background and hands-on experience with Python and R make me a strong candidate for this role." This approach shows that you've done your research, understand the role, and are eager to contribute.

The best way for Data Science Interns to end a cover letter is by expressing gratitude for the opportunity and showing enthusiasm for the potential role. You could say something like, "Thank you for considering my application. I am excited about the possibility of contributing to your team and believe that my skills in data analysis and machine learning would make a valuable addition. I look forward to the possibility of discussing my application with you further." This ending is polite, shows eagerness, and reiterates your relevant skills. It also opens the door for further communication, which is key in the application process.

A cover letter for a Data Science Intern should include the following elements: 1. Introduction: This is where you introduce yourself and mention the position you're applying for. You can also mention how you heard about the internship opportunity. 2. Education and Skills: As an intern, you may not have a lot of work experience, so it's important to highlight your education and skills. Mention your major, the courses you've taken that are relevant to data science, and any key skills you've developed. This could include knowledge of programming languages like Python or R, experience with machine learning algorithms, or expertise in data visualization tools. 3. Relevant Projects or Experience: If you've done any projects or have any experience that's relevant to data science, be sure to mention it. This could be a project you did for a class, a research project, or even a personal project. Explain what you did, the tools and techniques you used, and what the results were. 4. Passion for Data Science: Employers want to see that you're passionate about data science, so explain why you're interested in this field and what you hope to learn from the internship. 5. Understanding of the Company: Show that you've done your research and understand what the company does. If possible, mention how you could contribute to their work with your data science skills. 6. Closing: In the closing paragraph, express your enthusiasm for the potential opportunity to intern with them. Also, provide information on how they can contact you. Remember, your cover letter should not just repeat your resume. It should provide more depth and context to your experiences and interest in data science.

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Data scientist resume example, data engineer resume example, try our ai cover letter generator.

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CRediT author statement

CRediT (Contributor Roles Taxonomy) was introduced with the intention of recognizing individual author contributions, reducing authorship disputes and facilitating collaboration. The idea came about following a 2012 collaborative workshop led by Harvard University and the Wellcome Trust, with input from researchers, the International Committee of Medical Journal Editors (ICMJE) and publishers, including Elsevier, represented by Cell Press.

CRediT offers authors the opportunity to share an accurate and detailed description of their diverse contributions to the published work.

The corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors

The role(s) of all authors should be listed, using the relevant above categories

Authors may have contributed in multiple roles

CRediT in no way changes the journal’s criteria to qualify for authorship

CRediT statements should be provided during the submission process and will appear above the acknowledgment section of the published paper as shown further below.

*Reproduced from Brand et al. (2015), Learned Publishing 28(2), with permission of the authors.

Sample CRediT author statement

Zhang San:  Conceptualization, Methodology, Software  Priya Singh. : Data curation, Writing- Original draft preparation.  Wang Wu : Visualization, Investigation.  Jan Jansen :  Supervision. : Ajay Kumar : Software, Validation.:  Sun Qi:  Writing- Reviewing and Editing,

Read more about CRediT  here opens in new tab/window  or check out this  article from  Authors' Updat e:  CRediT where credit's due .

• Operating systems

When enterprise employees worked almost exclusively in company offices, they sat at desks and did their work on company-owned desktop computers. Now an employee's work moves with them from place to place. On laptops and tablets and phones. And very often, the work resides on devices the employees own. All this has shifted the work of the enterprise IT staff dramatically. This cavalcade of mobile devices and remote employees is one reason unified endpoint management came to be. In this guide, we look at what UEM can do, its main features, how to choose the right UEM product for your organization – and much more.

Operating system (os).

• Stephen J. Bigelow, Senior Technology Editor

What is an operating system?

An operating system (OS) is the program that, after being initially loaded into the computer by a boot program, manages all of the other application programs in a computer. The application programs make use of the operating system by making requests for services through a defined application program interface ( API ). In addition, users can interact directly with the operating system through a user interface, such as a command-line interface (CLI) or a graphical UI (GUI).

Why use an operating system?

An operating system brings powerful benefits to computer software and software development. Without an operating system, every application would need to include its own UI, as well as the comprehensive code needed to handle all low-level functionality of the underlying computer, such as disk storage, network interfaces and so on. Considering the vast array of underlying hardware available, this would vastly bloat the size of every application and make software development impractical.

Instead, many common tasks, such as sending a network packet or displaying text on a standard output device, such as a display, can be offloaded to system software that serves as an intermediary between the applications and the hardware. The system software provides a consistent and repeatable way for applications to interact with the hardware without the applications needing to know any details about the hardware.

As long as each application accesses the same resources and services in the same way, that system software -- the operating system -- can service almost any number of applications. This vastly reduces the amount of time and coding required to develop and debug an application, while ensuring that users can control, configure and manage the system hardware through a common and well-understood interface.

This article is part of

What is unified endpoint management (UEM)? A complete guide

• Which also includes:
• 7 key benefits of mobile device management for businesses
• Compare capabilities of Office 365 MDM vs. Intune
• How to successfully implement MDM for BYOD

Once installed, the operating system relies on a vast library of device drivers to tailor OS services to the specific hardware environment. Thus, every application may make a common call to a storage device, but the OS receives that call and uses the corresponding driver to translate the call into actions (commands) needed for the underlying hardware on that specific computer. Today, the operating system provides a comprehensive platform that identifies, configures and manages a range of hardware, including processors; memory devices and memory management; chipsets; storage; networking; port communication, such as Video Graphics Array (VGA), High-Definition Multimedia Interface (HDMI) and Universal Serial Bus (USB); and subsystem interfaces, such as Peripheral Component Interconnect Express (PCIe).

What are the functions of an operating system?

An operating system provides three essential capabilities: It offers a UI through a CLI or GUI; it launches and manages the application execution; and it identifies and exposes system hardware resources to those applications -- typically, through a standardized API.

UI. Every operating system requires a UI, enabling users and administrators to interact with the OS in order to set up, configure and even troubleshoot the operating system and its underlying hardware. There are two primary types of UI available: CLI and GUI.

The CLI, or terminal mode window , provides a text-based interface where users rely on the traditional keyboard to enter specific commands, parameters and arguments related to specific tasks. The GUI, or desktop , provides a visual interface based on icons and symbols where users rely on gestures delivered by human interface devices, such as touchpads, touchscreens and mouse devices.

The GUI is most frequently used by casual or end users that are primarily interested in manipulating files and applications, such as double-clicking a file icon to open the file in its default application. The CLI remains popular among advanced users and system administrators that must handle a series of highly granular and repetitive commands on a regular basis, such as creating and running scripts to set up new personal computers (PCs) for employees.

Application management. An operating system handles the launch and management of every application. This typically supports an array of behaviors, including timesharing multiple processes, or threads , so that various tasks can share the available processors' time; handling interruptions that applications produce to gain a processor's immediate attention, ensuring there is enough memory to execute the application and its corresponding data without interfering with other processes; carrying out error handling that can gracefully remove an application's processes; and performing memory management without disrupting other applications or the OS.

An operating system can also support APIs that enable applications to utilize OS and hardware functions without the need to know anything about the low-level OS or hardware state. As an example, a Windows API can enable a program to obtain input from a keyboard or mouse; create GUI elements, such as dialog windows and buttons; read and write files to a storage device; and more. Applications are almost always tailored to use the operating system on which the application intends to run.

Additionally, an operating system can perform the following services for applications:

• In a multitasking operating system, where multiple programs can be running at the same time, the OS determines which applications should run in what order and how much time should be allowed for each application before giving another application a turn.
• It handles input/output (I/O) to and from attached hardware devices, such as hard disks, printers and dial-up ports.
• It sends messages to each application or interactive user -- or to a system operator -- about the status of operation and any errors that may have occurred.
• It can offload the management of batch jobs -- for example, printing -- so that the initiating application is freed from this work.
• On computers that can provide parallel processing, an operating system can manage how to divide the program so that it runs on more than one processor at a time.

All major computer platforms (hardware and software) require, and sometimes include, an operating system, and operating systems must be developed with different features to meet the specific needs of various form factors.

Device management. An operating system is responsible for identifying, configuring, and providing applications with common access to underlying computer hardware devices. As the OS recognizes and identifies hardware, the OS will install corresponding device drivers that enable the OS and applications running on the OS to use the devices without any specific knowledge of the hardware or devices.

An operating system is responsible for identifying the correct printer and installing the appropriate printer drivers so that an application needs to only make calls to the printer without having to use codes or commands that are specific to that printer -- that is the operating system's job. The situation is similar for other devices, such as USB ports; networking ports; graphics devices, such as graphics processing units (GPUs); motherboard chipsets; and storage devices, such as Serial-Attached SCSI (SAS) disk adapters and disks that are formatted with a suitable file system.

The OS identifies and configures physical and logical devices for service and typically records them in a standardized structure, such as Windows Registry. Device manufacturers periodically patch and update drivers, and the OS should update them to ensure best device performance and security. When devices are replaced, the OS also installs and configures new drivers.

Operating system types and examples

Although the fundamental roles of an operating system are ubiquitous, there are countless operating systems that serve a wide range of hardware and user needs.

General-purpose operating system. A general-purpose OS represents an array of operating systems intended to run a multitude of applications on a broad selection of hardware, enabling a user to run one or more applications or tasks simultaneously. A general-purpose OS can be installed on many different desktop and laptop models and run applications from accounting systems to databases to web browsers to games. General-purpose operating systems typically focus on process (thread) and hardware management to ensure that applications can reliably share the wide range of computing hardware present.

Common desktop operating systems include the following:

• Windows is Microsoft's flagship operating system, the de facto standard for home and business computers. Introduced in 1985, the GUI-based OS has been released in many versions since then. The user-friendly Windows 95 was largely responsible for the rapid development of personal computing.
• Mac OS is the operating system for Apple's Macintosh line of PCs and workstations.
• Unix is a multiuser operating system designed for flexibility and adaptability. Originally developed in the 1970s, Unix was one of the first operating systems to be written in the C language .
• Linux is a Unix-like operating system that was designed to provide PC users a free or low-cost alternative. Linux has a reputation as an efficient and fast-performing system.

Mobile operating system. Mobile operating systems are designed to accommodate the unique needs of mobile computing and communication-centric devices, such as smartphones and tablets. Mobile devices typically offer limited computing resources compared to traditional PCs, and the OS must be scaled back in size and complexity in order to minimize its own resource use, while ensuring adequate resources for one or more applications running on the device. Mobile operating systems tend to emphasize efficient performance, user responsiveness and close attention to data handling tasks, such as supporting media streaming. Apple iOS and Google Android are examples of mobile operating systems.

Embedded operating system. Not all computing devices are general purpose. A huge assortment of dedicated devices -- including home digital assistants, automated teller machines (ATMs), airplane systems, retail point of sale (POS) terminals and internet of things (IoT) devices -- includes computers that require an operating system. The principal difference is that the associated computing device only does one major thing, so the OS is highly stripped down and dedicated to both performance and resilience. The OS should run quickly, not crash, and handle all errors gracefully in order to continue operating in all circumstances. In most cases, the OS is provided on a chip that is incorporated into the actual device. A medical device used in a patient's life support equipment, for example, will employ an embedded OS that must run reliably in order to keep the patient alive. Embedded Linux is one example of an embedded OS.

Network operating system. A network operating system (NOS) is another specialized OS intended to facilitate communication between devices operating on a local area network ( LAN ). A NOS provides the communication stack needed to understand network protocols in order to create, exchange and decompose network packets. Today, the concept of a specialized NOS is largely obsolete because other OS types largely handle network communication. Windows 10 and Windows Server 2019, for example, include comprehensive networking capabilities. The concept of a NOS is still used for some networking devices, such as routers, switches and firewalls, and manufacturers may employ proprietary NOSes, including Cisco Internetwork Operating System (IOS), RouterOS and ZyNOS.

Real-time operating system. When a computing device must interact with the real world within constant and repeatable time constraints, the device manufacturer may opt to use a real-time operating system ( RTOS ). For example, an industrial control system may direct the operations of a sprawling factory or power plant. Such a facility will produce signals from myriad sensors and also send signals to operate valves, actuators, motors and countless other devices. In these situations, the industrial control system must respond quickly and predictably to changing real-world conditions -- otherwise, disaster may result. An RTOS must function without buffering, processing latencies and other delays, which are perfectly acceptable in other types of operating systems. Two examples of RTOSes include FreeRTOS and VxWorks.

The differences between operating system types are not absolute, and some operating systems can share characteristics of others. For example, general-purpose operating systems routinely include the networking capabilities found in a traditional NOS. Similarly, an embedded operating system commonly includes attributes of an RTOS, while a mobile operating system can still typically run numerous apps simultaneously like other general-purpose operating systems.

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